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You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
1990), on linguisitic acquisition (by the use of Part-of-Speech filters hand-crafted by a linguist) (Oueslati, 1999) or, more frequently, on a combination of the two (Smadja, 1993; Kilgarriff and Tugwell, 2001, for example). Most of these endeavours have focused on purely statistical acquisition techniques (Church and Hanks, 'However, our interpretation of LFs in this work is much looser, since we admitted verbs that would not be considered to be members of true collocations as Mel'cuk et al. (1984 1999) define them, i.e. groups of lexical units that share a restricted cooccurrence relationship. On the other hand, other work has been carried out in order to acquire collocations.
Citation Sentence:
1990 ) , on linguisitic acquisition ( by the use of Part-of-Speech filters hand-crafted by a linguist ) ( Oueslati , 1999 ) or , more frequently , on a combination of the two ( Smadja , 1993 ; Kilgarriff and Tugwell , 2001 , for example ) .
Context after the citation:
It is worth noting that although these techniques are able to identify N-V pairs, they do not specify the relationship between N and V, nor are they capable of focusing on a subset of N-V pairs. The original acquisition methodology we present in the next section will allow us to overcome this limitation.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:29
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Intermedia is no more developed and nobody of us had the opportunity to try it (Landow, 1994). In fact, they were used expecially in academic writing with some success. Apart from wikis, blogs, and cognitive mapping, we were also inspired by the experiences of early hypertext writing tools, in particular Intermedia and Storyspace.
Citation Sentence:
Intermedia is no more developed and nobody of us had the opportunity to try it ( Landow , 1994 ) .
Context after the citation:
Storyspace is currently distributed by Eastgate (2005), and we have used it for a time. However, in our opinion Storyspace is a product of its time and in fact it isnât a web application. Although it is possible to label links, it lacks a lot of features we need. Moreover, no hypertext writing tool available is released under an open source licence.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:290
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Morphological alterations of a search term have a negative impact on the recall performance of an information retrieval (IR) system (Choueka, 1990; J¨appinen and Niemist¨o, 1988; Kraaij and Pohlmann, 1996), since they preclude a direct match between the search term proper and its morphological variants in the documents to be retrieved.
Citation Sentence:
Morphological alterations of a search term have a negative impact on the recall performance of an information retrieval ( IR ) system ( Choueka , 1990 ; J ¨ appinen and Niemist ¨ o , 1988 ; Kraaij and Pohlmann , 1996 ) , since they preclude a direct match between the search term proper and its morphological variants in the documents to be retrieved .
Context after the citation:
In order to cope with such variation, morphological analysis is concerned with the reverse processing of inflection (e.g., âsearch edâ, âsearch ingâ)1, derivation (e.g., âsearch erâ or âsearch ableâ) and composition (e.g., German âBlut hoch druckâ [âhigh blood pressureâ]). The goal is to map all occurring morphological variants to some canonical base form â e.g., âsearchâ in the examples from above. The efforts required for performing morphological analysis vary from language to language. For English, known for its limited number of inflection patterns, lexicon-free general-purpose stem1â â denotes the string concatenation operator.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:291
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
ment (Sarkar and Wintner, 1999; Doran et al., 2000; Makino et al., 1998). 1In this paper, we use the term LTAG to refer to FBLTAG, if not confusing. There have been many studies on parsing techniques (Poller and Becker, 1998; Flickinger et al., 2000), ones on disambiguation models (Chiang, 2000; Kanayama et al., 2000), and ones on programming/grammar-development environ-
Citation Sentence:
ment ( Sarkar and Wintner , 1999 ; Doran et al. , 2000 ; Makino et al. , 1998 ) .
Context after the citation:
These works are restricted to each closed community, and the relation between them is not well discussed. Investigating the relation will be apparently valuable for both communities. In this paper, we show that the strongly equivalent grammars enable the sharing of âparsing techniquesâ, which are dependent on each computational framework and have never been shared among HPSG and LTAG communities. We apply our system to the latest version of the XTAG English grammar (The XTAG Research Group, 2001), which is a large-scale FB-LTAG grammar.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:292
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Finally, feedback expressions (head nods and shakes) are successfully predicted from speech, prosody and eye gaze in interaction with Embodied Communication Agents as well as human communication (Fujie et al., 2004; Morency et al., 2005; Morency et al., 2007; Morency et al., 2009). Louwerse et al. (2006) and Louwerse et al. (2007) study the relation between eye gaze, facial expression, pauses and dialogue structure in annotated English map-task dialogues (Anderson et al., 1991) and find correlations between the various modalities both within and across speakers. Sridhar et al. (2009) obtain promising results in dialogue act tagging of the Switchboard-DAMSL corpus using lexical, syntactic and prosodic cues, while Gravano and Hirschberg (2009) examine the relation between particular acoustic and prosodic turn-yielding cues and turn taking in a large corpus of task-oriented dialogues.
Citation Sentence:
Finally , feedback expressions ( head nods and shakes ) are successfully predicted from speech , prosody and eye gaze in interaction with Embodied Communication Agents as well as human communication ( Fujie et al. , 2004 ; Morency et al. , 2005 ; Morency et al. , 2007 ; Morency et al. , 2009 ) .
Context after the citation:
Our work is in line with these studies, all of which focus on the relation between linguistic expressions, prosody, dialogue content and gestures. In this paper, we investigate how feedback expressions can be classified into different dialogue act categories based on prosodic and gesture features. Our data are made up by a collection of eight video-recorded map-task dialogues in Danish, which were annotated with phonetic and prosodic information. We find that prosodic features improve the classification of dialogue acts and that head gestures, where they occur, contribute to the semantic interpretation of feedback expressions.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:293
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012).
Citation Sentence:
Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information ( Andrews et al. , 2009 ; Steyvers , 2010 ; Feng and Lapata , 2010b ; Bruni et al. , 2011 ; Silberer and Lapata , 2012 ; Johns and Jones , 2012 ; Bruni et al. , 2012a ; Bruni et al. , 2012b ; Silberer et al. , 2013 ) .
Context after the citation:
Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the âmeaning of words is entirely given by other wordsâ (Bruni et al., 2012b). In this paper, we explore various ways to integrate new perceptual information through novel computational modeling of this grounded knowledge into a multimodal distributional model of word meaning. The model we rely on was originally developed by Andrews et al. (2009) and is based on a generalization of Latent Dirichlet Allocation.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:294
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
We also made use of the person-name/instance pairs automatically extracted by Fleischman et al. (2003).2 This data provides counts for pairs such as âEdwin Moses, hurdlerâ and âWilliam Farley, industrialist.â see that we can widen a highway, we learn that we can also widen a sidewalk, bridge, runway, etc. If we
Citation Sentence:
We also made use of the person-name/instance pairs automatically extracted by Fleischman et al. ( 2003 ) .2 This data provides counts for pairs such as `` Edwin Moses , hurdler '' and `` William Farley , industrialist . ''
Context after the citation:
We have features for all concepts and therefore learn their association with each verb.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:295
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Al-Adhaileh and Tang (2001) presented an approach for constructing a BKB based on the S-SSTC. S-SSTC very well suited for the construction of a BKB, which is needed for the EBMT applications. The proposed S-SSTC annotation schema can fulfill this need, and it is flexible enough to handle different type of relations that may happen between different languagesâ structures.
Citation Sentence:
Al-Adhaileh and Tang ( 2001 ) presented an approach for constructing a BKB based on the S-SSTC .
Context after the citation:
In S-SSTC, the synchronous correspondence is defined in a way to ensure a flexible representation for both lexical and structural correspondences: iNodeâtoânode correspondence (lexical correspondence), which is recorded in terms of pair of intervals (Xs,Xt) where Xs and Xt is SNODE interval/s for the source and the target SSTC respectively, iiSubtreeâ toâSubtree correspondence (structural correspondence), which is very much needed for relating the two different languages at a level higher than the lexical level, a level of phrases. It is recorded in terms of pair of intervals (Ys,Yt) where Ys and Yt is STREE interval/s for the source and the target SSTC respectively. Furthermore, the SSTC structure can easily be extended to keep multiple levels of linguistic information, if they are considered important to enhance the performance of the machine translation system (i.e. Features transfer). For instance, each node representing a word in the annotated tree structure can be tagged with part of speech (POS), semantic features and morphological features.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:296
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
However, since work in this direction has started, a significant progress has also been made in the research on statistical learning of full parsers, both in terms of accuracy and processing time (Charniak, 1997b; Charniak, 1997a; Collins, 1997; Ratnaparkhi, 1997). Overall, the driving force behind the work on learning shallow parsers was the desire to get better performance and higher reliability. Finally, the hope behind this research direction was that this incremental and modular processing might result in more robust parsing decisions, especially in cases of spoken language or other cases in which the quality of the natural language inputs is low sentences which may have repeated words, missing words, or any other lexical and syntactic mistakes.
Citation Sentence:
However , since work in this direction has started , a significant progress has also been made in the research on statistical learning of full parsers , both in terms of accuracy and processing time ( Charniak , 1997b ; Charniak , 1997a ; Collins , 1997 ; Ratnaparkhi , 1997 ) .
Context after the citation:
This paper investigates the question of whether work on shallow parsing is worthwhile. That is, we attempt to evaluate quantitatively the intuitions described above that learning to perform shallow parsing could be more accurate and more robust than learning to generate full parses. We do that by concentrating on the task of identifying the phrase structure of sentences a byproduct of full parsers that can also be produced by shallow parsers. We investigate two instantiations of this task, âchuckingâ and identifying atomic phrases.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:297
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Also, advanced methods often require many training iterations, for example active learning (Dagan and Engelson,1995) and co-training (Blum and Mitchell, 1998). Efficient training is required because the amount of data available for training will increase significantly. Efficiency is required both in training and processing.
Citation Sentence:
Also , advanced methods often require many training iterations , for example active learning ( Dagan and Engelson ,1995 ) and co-training ( Blum and Mitchell , 1998 ) .
Context after the citation:
Processing text needs to be extremely efficient since many new applications will require very large quantities of text to be processed or many smaller quantities of text to be processed very quickly. State of the art accuracy is also important, particularly on complex systems since the error is accumulated from each component in the system. There is a speed/accuracy tradeoff that is rarely addressed in the literature. For instance, reducing the beam search width used for tagging can increase the speed without significantly reducing accuracy.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:298
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Andrews et al. (2009) furthered this work by showing that a bimodal topic model, consisting of both text and feature norms, outperformed models using only one modality on the prediction of association norms, word substitution errors, and semantic interference tasks. Griffiths et al. (2007) helped pave the path for cognitive-linguistic multimodal research, showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis (Deerwester et al., 1990) in the prediction of association norms. cue word and name the first (or several) associated words that come to mind (e.g., Nelson et al. (2004)), and feature norms, where subjects are given a cue word and asked to describe typical properties of the cue concept (e.g., McRae et al. (2005)).
Citation Sentence:
Andrews et al. ( 2009 ) furthered this work by showing that a bimodal topic model , consisting of both text and feature norms , outperformed models using only one modality on the prediction of association norms , word substitution errors , and semantic interference tasks .
Context after the citation:
In a similar vein, Steyvers (2010) showed that a different feature-topic model improved predictions on a fill-in-the-blank task. Johns and Jones (2012) take an entirely different approach by showing that one can successfully infer held out feature norms from weighted mixtures based on textual similarity. Silberer and Lapata (2012) introduce a new method of multimodal integration based on Canonical Correlation Analysis, and performs a systematic comparison between their CCA-based model and others on association norm prediction, held out feature prediction, and word similarity. As computer vision techniques have improved over the past decade, other research has begun directly using visual information in place of feature norms.
|
Extends
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:299
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
The following four components have been identified as the key elements of a question related to patient care (Richardson et al. 1995): The second facet is independent of the clinical task and pertains to the structure of a well-built clinical question. The results of this research are implemented in the PubMed Clinical Queries tools, which can be used to retrieve task-specific citations (more about this in the next section).
Citation Sentence:
The following four components have been identified as the key elements of a question related to patient care ( Richardson et al. 1995 ) :
Context after the citation:
⢠What is the primary problem or disease? What are the characteristics of the patient (e.g., age, gender, or co-existing conditions)? ⢠What is the main intervention (e.g., a diagnostic test, medication, or therapeutic procedure)? ⢠What is the main intervention compared to (e.g., no intervention, another drug, another therapeutic procedure, or a placebo)?
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:3
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Samuelsson and Voutilainen (1997) report excellent part-of-speech tagging results using a handcrafted approach that is close to OT.3 More speculatively, imagine an OT grammar for stylistic revision of parsed sentences. In some cases rankings may work well enough. Second, weights are an annoyance when writing grammars by hand.
Citation Sentence:
Samuelsson and Voutilainen ( 1997 ) report excellent part-of-speech tagging results using a handcrafted approach that is close to OT .3 More speculatively , imagine an OT grammar for stylistic revision of parsed sentences .
Context after the citation:
The tension between preserving the original author's text (faithfulness to the underlying form) and making it readable in various ways (well-formedness) is right up OT's alley. The same applies to document layout: I have often wished I could write OT-style TeX macros! Third, even in statistical corpus-based NLP, estimating a full Gibbs distribution is not always feasible. Even if strict ranking is not quite accurate, sparse data or the complexity of parameter estimation may make it easier to learn a good OT grammar than a good arbitrary Gibbs model.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:30
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
It can be shown (Berger et al., 1996) that the use of this model with maximum likelihood parameter estimation is justified on information-theoretic grounds when q represents some prior knowledge about the true distribution and when the expected values of f in the training corpus are identical to their true expected values.3 There is no requirement that the components of f represent disjoint or statistically independent events. However, since the models tested apparently differed in other aspects, it is hard to determine how much of this gain can be attributed to the use of ME. 2Rosenfeld (1996) reports a greater perplexity reduction (23% versus 10%) over a baseline trigram language model due the use of ME versus linear word triggers.
Citation Sentence:
It can be shown ( Berger et al. , 1996 ) that the use of this model with maximum likelihood parameter estimation is justified on information-theoretic grounds when q represents some prior knowledge about the true distribution and when the expected values of f in the training corpus are identical to their true expected values .3 There is no requirement that the components of f represent disjoint or statistically independent events .
Context after the citation:
This result motivates the use of MEMD models, but it offers only weak guidance on how to select q or f. In practice, q is usually chosen on the basis of efficiency considerations (when the information it captures would be computationally expensive to represent as components of f), and f is established using heuristics such as described in the next section. Once q and f have been chosen, the ITS algorithm (Della Pietra et al., 1995) can be used to find maximum likelihood parameter values. In the current context, since the aim was to compare equivalent linear and MEMD models, I used an interpolated trigram as the reference distribution q and boolean indicator functions over bilingual word pairs as features (ie, components of f).
|
Motivation
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:300
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Shortly after the publication of The Sound Pattern of English (Chomsky and Halle 1968), Kornai points out, "Johnson (1970) demonstrated that the context-sensitive machinery of SPE . . . [could] be replaced by a much simpler one, based on finite-state transducers (FSTs); the same conclusion was reached independently by Kaplan and Kay, whose work remained an underground classic until it was finally published in Kaplan and Kay (1994)." This system extracts relational information, such as "who is where" or "who bought what", from issues of the Wall Street Journal (source code and sample data are included on the CD-ROM). In this regard, Kornai's own chapter on vectorized finite-state automata describes an extremely efficient pattern-matching engine, around which the NewsMonitor system is built.
Citation Sentence:
Shortly after the publication of The Sound Pattern of English ( Chomsky and Halle 1968 ) , Kornai points out , `` Johnson ( 1970 ) demonstrated that the context-sensitive machinery of SPE ... [ could ] be replaced by a much simpler one , based on finite-state transducers ( FSTs ) ; the same conclusion was reached independently by Kaplan and Kay , whose work remained an underground classic until it was finally published in Kaplan and Kay ( 1994 ) . ''
Context after the citation:
These works inspired Koskenniemi's two-level system, and the Xerox rule compiler (Dalrymple et al. 1987). Both are now dominant tools in the fields of computational phonology and morphology, as exemplified by Tateno et al. (Chapter 6), "The Japanese lexical transducer based on stem-suffix style forms" and Kim and Jang (Chapter 7), "Acquiring rules for reducing morphological ambiguity from POS tagged corpus in Korean." The latter includes an algorithm for automatically inferring regular grammar rules for morphological relations directly from part-of-speech tagged corpora. Although finite-state approaches to NLP were attempted as early as 1958, Kornai comments that finite-state syntax "did not really come in from the cold until the nineties."
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:301
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
18 In this article, we use a newer version of the corpus by Alkuhlani and Habash (2011) than the one we used in Marton, Habash, and Rambow (2011). 17 We also applied the manipulations described in Section A.3 to FNNUM, giving us the variants FNNUMDGT and FNNUMDGTBIN, which we tested similarly. 16 http://sourceforge.net/projects/elixir-fm.
Citation Sentence:
18 In this article , we use a newer version of the corpus by Alkuhlani and Habash ( 2011 ) than the one we used in Marton , Habash , and Rambow ( 2011 ) .
Context after the citation:
19 The paper by Alkuhlani and Habash (2012) presents additional, more sophisticated models that we do not use in this article. shrunk with these functional feature combinations to 0.3%. We take it as further support to the relevance of our functional morphology features, and their partial redundancy with the form-based morphological information embedded in the CATIBEX POS tags.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:302
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Atallah et al. (2001b) and Topkara et al. (2006a) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method. After embedding the secret message, modified deep structure forms are converted into the surface structure format via language generation tools. Liu et al. (2005), Meral et al. (2007), Murphy (2001), Murphy and Vogel (2007) and Topkara et al. (2006a) all belong to the syntactic transformation category.
Citation Sentence:
Atallah et al. ( 2001b ) and Topkara et al. ( 2006a ) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method .
Context after the citation:
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:303
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Another possibility that often works better is to use Minimum Bayes-Risk (MBR) decoding (Kumar and Byrne 2002; Liang, Taskar, and Klein 2006; Ganchev, and Taskar 2007). lattice).
Citation Sentence:
Another possibility that often works better is to use Minimum Bayes-Risk ( MBR ) decoding ( Kumar and Byrne 2002 ; Liang , Taskar , and Klein 2006 ; Ganchev , and Taskar 2007 ) .
Context after the citation:
Using this decoding we include an alignment link i j if the posterior probability that word i aligns to word j is above some threshold. This allows the accumulation of probability from several low-scoring alignments that agree on one alignment link. The threshold is tuned on some small amount of labeled dataâin our case the development setâto some loss. Kumar and Byrne (2002) study different loss functions that incorporate linguisti HMMâs
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:304
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
The subcategorization requirements expressed by semantic forms are enforced at f-structure level through completeness and coherence well-formedness conditions on f-structure (Kaplan and Bresnan 1982): An f-structure is locally complete iff it contains all the governable grammatical functions that its predicate governs. In LFG, the subcategorization requirements of a particular predicate are expressed by its semantic form: FOCUS((r SUBJ)(r OBLon)) in Figure 1. of phrase structural position.
Citation Sentence:
The subcategorization requirements expressed by semantic forms are enforced at f-structure level through completeness and coherence well-formedness conditions on f-structure ( Kaplan and Bresnan 1982 ) : An f-structure is locally complete iff it contains all the governable grammatical functions that its predicate governs .
Context after the citation:
An f-structure is complete iff it and all its subsidiary f-structures are locally complete. An f-structure is locally coherent iff all the governable grammatical functions that it contains are governed by a local predicate. An f-structure is coherent iff it and all its subsidiary f-structures are locally coherent. (page 211) Consider again the f-structure in Figure 2.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:305
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar (FB-LTAG1) (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) and Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) by a method of grammar conversion.
Citation Sentence:
This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar ( FB-LTAG1 ) ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) and Head-Driven Phrase Structure Grammar ( HPSG ) ( Pollard and Sag , 1994 ) by a method of grammar conversion .
Context after the citation:
The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar (Yoshinaga and Miyao, 2001). Strong equivalence means that both grammars generate exactly equivalent parse results, and that we can share the LTAG grammars and lexicons in HPSG applications. Our system can reduce considerable workload to develop a huge resource (grammars and lexicons) from scratch. Our concern is, however, not limited to the sharing of grammars and lexicons.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:306
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Following the example of Landow (1994), we will call the autonomous units of a hypertext lexias (from âlexiconâ), a word coined by Roland Barthes (1970). Our aim is to use the analysis of hypertexts for interesting insights, useful for blogs and wikis too. We consider hypertexts as parents of blogs and wikis.
Citation Sentence:
Following the example of Landow ( 1994 ) , we will call the autonomous units of a hypertext lexias ( from ` lexicon ' ) , a word coined by Roland Barthes ( 1970 ) .
Context after the citation:
Consequently, a hypertext is a set of lexias. In hypertexts transitions from one lexia to another are not necessarily sequential, but navigational. The main problems of hypertexts, acknowledged since the beginning, have been traced as follows (Nelson, 1992): ⢠The framing problem, i.e. creating arbitrary closed contexts of very large document collections.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:307
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
(Chomsky 1977). Stephanie Seneff TINA: A Natural Language System for Spoken Language Applications subject-tagging for verbs, and long distance movement (often referred to as gaps, or the trace, as in "(which article), do you think I should read (t1)?") These include agreement constraints, semantic restrictions,
Citation Sentence:
( Chomsky 1977 ) .
Context after the citation:
The gap mechanism resembles the Hold register idea of ATNs (Woods 1970) and the treatment of bounded domination metavariables in lexical functional grammars (LFGs) (Bresnan 1982, p. 235 ff.) , but it is different from these in that the process of filling the Hold register equivalent involves two steps separately initiated by two independent nodes. Our approach to the design of a constraint mechanism is to establish a framework general enough to handle syntactic, semantic, and, ultimately, phonological constraints using identical functional procedures applied at the node level. The intent was to design a grammar for which the rules would be kept completely free of any constraints.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:308
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
We use the same set of binary features as in previous work on this dataset (Pang et al., 2002; Pang and Lee, 2004; Zaidan et al., 2007). where ~f(·) extracts a feature vector from a classified ~ document, θ are the corresponding weights of those features, and Zθ(x) def � Ey u(x, y) is a normalizer. A CRF is just another conditional log-linear model:
Citation Sentence:
We use the same set of binary features as in previous work on this dataset ( Pang et al. , 2002 ; Pang and Lee , 2004 ; Zaidan et al. , 2007 ) .
Context after the citation:
Specifically, let V = {v1, ..., v177441 be the set of word types with count > 4 in the full 2000-document corpus. Define fh(x, y) to be y if vh appears at least once in x, and 0 otherwise. Thus θ E 817744, and positive weights in θ favor class label y = +1 and equally discourage y = -1, while negative weights do the opposite. This standard unigram feature set is linguistically impoverished, but serves as a good starting point for studying rationales.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:309
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Thus for instance, (Copestake and Flickinger, 2000; Copestake et al., 2001) describes a Head Driven Phrase Structure Grammar (HPSG) which supports the parallel construction of a phrase structure (or derived) tree and of a semantic representation and (Dalrymple, 1999) show how to equip Lexical Functional grammar (LFG) with a glue semantics. âSemantic grammarsâ already exist which describe not only the syntax but also the semantics of natural language.
Citation Sentence:
Thus for instance , ( Copestake and Flickinger , 2000 ; Copestake et al. , 2001 ) describes a Head Driven Phrase Structure Grammar ( HPSG ) which supports the parallel construction of a phrase structure ( or derived ) tree and of a semantic representation and ( Dalrymple , 1999 ) show how to equip Lexical Functional grammar ( LFG ) with a glue semantics .
Context after the citation:
These grammars are both efficient and large scale in that they cover an important fragment of the natural language they describe and can be processed by parsers and generators in almost real time. For instance, the LFG grammar parses sentences from the Wall Street Journal and the ERG HPSG grammar will produce semantic representations for about 83 per cent of the utterances in a corpus of some 10 000 utterances varying in length between one and thirty words. Parsing times vary between a few ms for short sentences and several tens of seconds for longer ones. Nonetheless, from a semantics viewpoint, these grammars fail to yield a clear account of the paraphrastic relation.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:31
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Various feature selection techniques have been used in automatic text categorization; they include document frequency (DF), information gain (IG) (Tzeras and Hartman, 1993), minimum description length principal (Lang, 1995), and the X2 statistic. Feature selection techniques have been widely used in information retrieval as a means for coping with the large number of words in a document; a selection is made to keep only the more relevant words. Cross-validation, using feature selection
Citation Sentence:
Various feature selection techniques have been used in automatic text categorization ; they include document frequency ( DF ) , information gain ( IG ) ( Tzeras and Hartman , 1993 ) , minimum description length principal ( Lang , 1995 ) , and the X2 statistic .
Context after the citation:
(Yang and Pedersen, 1997) has found strong correlations between DF, IG and the X2 statistic for a term. On the other hand, (Rogati and Yang, 2002) reports the X2 to produce best performance. In this paper, we use TF-IDF (a kind of augmented DF) as a feature selection criterion, in order to ensure results are comparable with those in (Yahyaoui, 2001). TF-IDF (term frequency-inverse document frequency) is one of the widely used feature selection techniques in information retrieval (Yates and Neto, 1999).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:310
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking, allowing the automatic analysis of the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon et al., 2006).
Citation Sentence:
Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking , allowing the automatic analysis of the opinions that people submit ( Shulman et al. , 2005 ; Cardie et al. , 2006 ; Kwon et al. , 2006 ) .
Context after the citation:
There has also been work focused upon determining the political leaning (e.g., âliberalâ vs. âconservativeâ) of a document or author, where most previously-proposed methods make no direct use of relationships between the documents to be classified (the âunlabeledâ texts) (Laver et al., 2003; Efron, 2004; Mullen and Malouf, 2006). An exception is Grefenstette et al. (2004), who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site. Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:311
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Petrov et al. (2010) observed that dependency parsers tend to do quite poorly when parsing questions due to their limited exposure to them in the news corpora from the PennTreebank. Consider, for example, the case of questions. Another application of the augmented-loss framework is to improve parser domain portability in the presence of partially labeled data.
Citation Sentence:
Petrov et al. ( 2010 ) observed that dependency parsers tend to do quite poorly when parsing questions due to their limited exposure to them in the news corpora from the PennTreebank .
Context after the citation:
Table 2 shows the accuracy of two parsers (LAS, UAS and the F1 of the root dependency attachment) on the QuestionBank test data. The first is a parser trained on the standard training sections of the PennTreebank (PTB) and the second is a parser trained on the training portion of the QuestionBank (QTB). Results for both transition-based parsers and graph-based parsers are given.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:312
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Lin (1998a)âs similar word list for eat misses these but includes sleep (ranked 6) and sit (ranked 14), because these have similar subjects to eat. The DSP parameters for eat, for example, place high weight on features like Pr(nlbraise), Pr(nlration), and Pr(nlgarnish). In particular, the weights on the verb co-occurrence features (Section 3.3.1) provide a high-quality, argument-specific similarityranking of other verb contexts.
Citation Sentence:
Lin ( 1998a ) 's similar word list for eat misses these but includes sleep ( ranked 6 ) and sit ( ranked 14 ) , because these have similar subjects to eat .
Context after the citation:
Discriminative, context-specific training seems to yield a better set of similar predicates, e.g. the highest-ranked contexts for DSPâoo, on the verb join,3 lead 1.42, rejoin 1.39, form 1.34, belong to 1.31, found 1.31, quit 1.29, guide 1.19, induct 1.19, launch (subj) 1.18, work at 1.14 give a better SIMS(join) for Equation (1) than the top similarities returned by (Lin, 1998a): participate 0.164, lead 0.150, return to 0.148, say 0.143, rejoin 0.142, sign 0.142, meet 0.142, include 0.141, leave 0.140, work 0.137 Other features are also weighted intuitively. Note that case is a strong indicator for some arguments, for example the weight on being lower-case is high for become (0.972) and eat (0.505), but highly negative for accuse (-0.675) and embroil (-0.573) which often take names of people and organizations.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:313
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
⢠Learnability (Zernik and Dyer 1987) ⢠Text generation (Hovy 1988; Milosavljevic, Tulloch, and Dale 1996) ⢠Speech generation (Rayner and Carter 1997) ⢠Localization (Sch¨aler 1996) More specifically, the notion of the phrasal lexicon (used first by Becker 1975) has been used successfully in a number of areas: Accordingly, they generate lexical correspondences by means of co-occurrence measures and string similarity metrics.
Citation Sentence:
⢠Learnability ( Zernik and Dyer 1987 ) ⢠Text generation ( Hovy 1988 ; Milosavljevic , Tulloch , and Dale 1996 ) ⢠Speech generation ( Rayner and Carter 1997 ) ⢠Localization ( Sch ¨ aler 1996 )
Context after the citation:
More recently, Simard and Langlais (2001) have proposed the exploitation of TMs at a subsentential level, while Carl, Way, and Sch¨aler (2002) and Sch¨aler, Way, and Carl (2003, pages 108â109) describe how phrasal lexicons might come to occupy a central place in a future hybrid integrated translation environment. This, they suggest, may result in a paradigm shift from TM to EBMT via the phrasal lexicon: Translators are on the whole wary of MT technology, but once subsentential alignment is enabled, translators will become aware of the benefits to be gained from (source, target) phrasal segments, and from there they suggest that âit is a reasonably short step to enabling an automated solution via the recombination element of EBMT systems such as those described in [Carl and Way 2003].â In this section, we describe how the memory of our EBMT system is seeded with a set of translations obtained from Web-based MT systems. From this initial resource, we subsequently derive a number of different databases that together allow many new input sentences to be translated that it would not be possible to translate in other systems.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:314
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
For example, a âweb pageâ is more similar to an infinite canvas than a written page (McCloud, 2001). Nowadays the use of computers for writing has drammatically changed, expecially after their interconnection via the internet, since at least the foundation of the web (Berners-Lee, 1999). For example, when books shouldnât be copied by hand any longer, authors took the advantage and start writing original books and evaluation â i.e. literary criticism â unlike in the previous times (Eisenstein, 1983).
Citation Sentence:
For example , a ` web page ' is more similar to an infinite canvas than a written page ( McCloud , 2001 ) .
Context after the citation:
Moreover, what seems to be lost is the relations, like the texture underpinning the text itself. From a positive point of view these new forms of writing may realize the postmodernist and decostructionist dreams of an âopera apertaâ (open work), as Eco would define it (1962). From a more pessimistic one, an author may feel to have lost power in this openness. Henceforth the collaborative traits of blogs and wikis (McNeill, 2005) emphasize annotation, comment, and strong editing.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:315
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary, i.e., mouse Mouse Genome Database (MGD) [18], fly FlyBase [19], yeast Saccharomyces Genome Database (SGD) [20], rat â Rat Genome Database (RGD) [21], worm â WormBase [22], Human Nomenclature Database (HUGO) [23], Online Mendelian Inheritance in Man (OMIM) [24], and Enzyme Nomenclature Database (ECNUM) [25, 26]. The Semantic Network contains information about the types or categories (e.g., âDisease or Syndromeâ, âVirusâ) to which all META concepts have been assigned. The SPECIALIST lexicon contains syntactic information for many terms, component words, and English words, including verbs, which do not appear in the META.
Citation Sentence:
Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] .
Context after the citation:
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:316
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
2This view of typed feature structures differs from the perspective on typed feature structures as modeling partial information as in (Carpenter, 1992). Typed feature grammars can be used as the basis for implementations of Head-driven Phrase Structure Grammar (Pollard and Sag, 1994).3 (Meurers and Minnen, 1997) propose a compilation of lexical rules into TIT definite clauses For expository reasons we represent the ARG n features of the append relation as separate arguments.
Citation Sentence:
2This view of typed feature structures differs from the perspective on typed feature structures as modeling partial information as in ( Carpenter , 1992 ) .
Context after the citation:
Typed feature structures as normal form TFC terms are merely syntactic objects. 'See (King, 1994) for a discussion of the appropriateness of TIG for HPSG and a comparison with other feature logic approaches designed for HPSG. which are used to restrict lexical entries. (Graz and Meurers, 1997b) describe a method for compiling implicational constraints into typed feature grammars and interleaving them with relational constraints.4 Because of space limitations we have to refrain from an example.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:317
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
This approach resembles the work by Grishman et al. (1986) and Hirschman et al. (1975) on selectional restrictions. Then the system could propose to a human expert a set of filters for each node, based on its observations, and the human could make the final decision on whether to accept the proposals. In principle, one could parse a large set of sentences with semantics turned off, collecting the semantic conditions that occurred at each node of interest.
Citation Sentence:
This approach resembles the work by Grishman et al. ( 1986 ) and Hirschman et al. ( 1975 ) on selectional restrictions .
Context after the citation:
The semantic conditions that pass could even ultimately be associated with probabilities, obtained by frequency counts on their occurrences. There is obviously a great deal more work to be done in this important area.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:318
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
This is noticeable for German (Brants et al., 2002) and Portuguese (Afonso et al., 2002), which still have high overall accuracy thanks to very high attachment scores, but much more conspicuous for Czech (B¨ohmov´a et al., 2003), Dutch (van der Beek et al., 2002) and Slovene (DËzeroski et al., 2006), where root precision drops more drastically to about 69%, 71% and 41%, respectively, and root recall is also affected negatively. A second observation is that a high proportion of non-projective structures leads to fragmentation in the parser output, reflected in lower precision for roots. Japanese (Kawata and Bartels, 2000), despite a very high accuracy, is different in that attachment score drops from 98% to 85%, as we go from length 1 to 2, which may have something to do with the data consisting of transcribed speech with very short utterances.
Citation Sentence:
This is noticeable for German ( Brants et al. , 2002 ) and Portuguese ( Afonso et al. , 2002 ) , which still have high overall accuracy thanks to very high attachment scores , but much more conspicuous for Czech ( B ¨ ohmov ´ a et al. , 2003 ) , Dutch ( van der Beek et al. , 2002 ) and Slovene ( DËzeroski et al. , 2006 ) , where root precision drops more drastically to about 69 % , 71 % and 41 % , respectively , and root recall is also affected negatively .
Context after the citation:
On the other hand, all three languages behave like high-accuracy languages with respect to attachment score. A very similar pattern is found for Spanish (Civit Torruella and MartiAntonin, 2002), although this cannot be explained by a high proportion of non-projective structures. One possible explanation in this case may be the fact that dependency graphs in the Spanish data are sparsely labeled, which may cause problem for a parser that relies on dependency labels as features. The results for Arabic (HajiËc et al., 2004; SmrËz et al., 2002) are characterized by low root accuracy
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:319
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
While many linguistic theories state subcategorization requirements in terms of phrase structure (CFG categories), Dalrymple (2001) questions the viability and universality of such an approach because of the variety of ways in which grammatical functions may be realized at the language-specific constituent structure level. The subject is instead specified externally in the matrix phrase: The judge wants [XCOMP to open an inquiry]. XCOMP and XADJ are open functions not requiring an internal SUBJ.
Citation Sentence:
While many linguistic theories state subcategorization requirements in terms of phrase structure ( CFG categories ) , Dalrymple ( 2001 ) questions the viability and universality of such an approach because of the variety of ways in which grammatical functions may be realized at the language-specific constituent structure level .
Context after the citation:
LFG argues that subcategorization requirements are best stated at the f-structure level, in functional rather than phrasal terms. This is because of the assumption that abstract grammatical functions are primitive concepts as opposed to derivatives Cand f-structures for Penn Treebank sentence wsj 0267 72, The inquiry soon focused on the judge. of phrase structural position.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:32
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
LTAG (Schabes et al., 1988) is a grammar formalism that provides syntactic analyses for a sentence by composing elementary trees with two opera-
Citation Sentence:
LTAG ( Schabes et al. , 1988 ) is a grammar formalism that provides syntactic analyses for a sentence by composing elementary trees with two opera -
Context after the citation:
tions called substitution and adjunction. Elementary trees are classified into two types, initial trees and auxiliary trees (Figure 2). An elementary tree has at least one leaf node labeled with a terminal symbol called an anchor (marked with o). In an auxiliary tree, one leaf node is labeled with the same symbol as the root node and is specially marked as afoot node (marked with *).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:320
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Translations have been generated by the CrowdFlower3 channel to Amazon Mechanical Turk4 (MTurk), adopting the methodology proposed by (Negri and Mehdad, 2010). It consists of 1600 pairs derived from the RTE3 development and test sets (800+800). The dataset used for our experiments is an EnglishSpanish entailment corpus obtained from the original RTE3 dataset by translating the English hypothesis into Spanish.
Citation Sentence:
Translations have been generated by the CrowdFlower3 channel to Amazon Mechanical Turk4 ( MTurk ) , adopting the methodology proposed by ( Negri and Mehdad , 2010 ) .
Context after the citation:
The method relies on translation-validation cycles, defined as separate jobs routed to MTurkâs workforce. Translation jobs return one Spanish version for each hypothesis. Validation jobs ask multiple workers to check the correctness of each translation using the original English sentence as reference. At each cycle, the translated hypothesis accepted by the majority of trustful validators5 are stored in the CLTE corpus, while wrong translations are sent back to workers in a new translation job.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:321
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
This work is a continuation of that initiated in (Yahyaoui, 2001), which reports an overall NB classification correctness of 75.6%, in cross validation experiments, on a data set that consists of 100 documents for each of 12 categories (the data set is collected from different Arabic portals). The aim of this work is to gain some insight as to whether Arabic document categorization (using NB) is sensitive to the root extraction algorithm used or to different data sets. The present work evaluates the performance on Arabic documents of the Naïve Bayes algorithm (NB) one of the simplest algorithms applied to English document categorization (Mitchell, 1997).
Citation Sentence:
This work is a continuation of that initiated in ( Yahyaoui , 2001 ) , which reports an overall NB classification correctness of 75.6 % , in cross validation experiments , on a data set that consists of 100 documents for each of 12 categories ( the data set is collected from different Arabic portals ) .
Context after the citation:
A 50% overall classification accuracy is also reported when testing with a separately collected evaluation set (3 documents for each of the 12 categories). The present work expands the work in (Yahyaoui, 2001) by experimenting with the use of a better root extraction algorithm (El Kourdi, 2004) for document preprocessing, and using a different data set, collected from the largest Arabic site on the web: aljazeera.net.
|
Extends
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:322
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Our previous work (Zhai et al., 2012) designed an EMbased method to construct unsupervised trees for tree-based translation models. For unsupervised tree structure induction, DeNero and Uszkoreit (2011) adopted a parallel parsing model to induce unlabeled trees of source sentences for syntactic pre-reordering. In this study, we move in a new direction to build a tree-based translation model with effective unsupervised U-tree structures.
Citation Sentence:
Our previous work ( Zhai et al. , 2012 ) designed an EMbased method to construct unsupervised trees for tree-based translation models .
Context after the citation:
This work differs from the above work in that we design a novel Bayesian model to induce unsupervised U-trees, and prior knowledge can be encoded into the model more freely and effectively. Blunsom et al. (2008, 2009, 2010) utilized Bayesian methods to learn synchronous context free grammars (SCFG) from a parallel corpus. The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system (Chiang, 2007). Levenberg et al. (2012) employed a Bayesian method to learn discontinuous SCFG rules.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:323
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Then, we binarize the English parse trees using the head binarization approach (Wang et al., 2007) and use the resulting binary parse trees to build another s2t system. To build the above s2t system, we first use the parse tree, which is generated by parsing the English side of the bilingual data with the Berkeley parser (Petrov et al., 2006). We then obtain the composed rules by composing two or three adjacent minimal rules.
Citation Sentence:
Then , we binarize the English parse trees using the head binarization approach ( Wang et al. , 2007 ) and use the resulting binary parse trees to build another s2t system .
Context after the citation:
For the U-trees, we run the Gibbs sampler for 1000 iterations on the whole corpus. The sampler uses 1,087s per iteration, on average, using a single core, 2.3 GHz Intel Xeon machine. For the hyperparameters, we set Ä® to 0.1 and pexpmrd = 1/3 to give a preference to the rules with small fragments. We built an s2t translation system with the achieved U-trees after the 1000th iteration.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:324
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
This is the strongest version of the sorites paradox (e.g., Hyde 2002). A positive answer would not be psychologically plausible, since x and y are indistinguishable; but a negative answer would prohibit any binary distinction between objects that are large and objects that are not, given that one can always construct objects x and y, one of which falls just below the divide while the other falls just above it. Consider the question, discussed in the philosophical logic literature, of whether it is legitimate, for a gradable adjective, to distinguish between âobservationally indifferentâ entities: Suppose two objects x and y, are so similar that it is impossible to distinguish their sizes; can it ever be reasonable to say that x is large and y is not?
Citation Sentence:
This is the strongest version of the sorites paradox ( e.g. , Hyde 2002 ) .
Context after the citation:
Our approach to vague descriptions allows a subtle response: that the offending statement may be correct yet infelicitous. This shifts the problem from asking when vague descriptions are âcorrectâ to the question of when they are used felicitously. Felicity is naturally thought of as a gradable concept. There is therefore no need for a generator to demarcate precisely between felicitous and infelicitous expressions, as long as all the utterances generated are felicitous enough.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:325
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
We used a standard implementation of IBM Model 4 (Och and Ney 2003) and because changing the existing code is not trivial, we could not use the same stopping criterion to avoid overfitting and we are not able to produce precision/recall curves. Because our approach is orthogonal to the base model used, the constraints described here could be applied in principle to IBM Model 4 if exact inference was efficient, hopefully yielding similar improvements. However, we would like to note that IBM Model 4 is a more complex model, able to capture more structure, albeit at the cost of intractable inference.
Citation Sentence:
We used a standard implementation of IBM Model 4 ( Och and Ney 2003 ) and because changing the existing code is not trivial , we could not use the same stopping criterion to avoid overfitting and we are not able to produce precision/recall curves .
Context after the citation:
We trained IBM Model 4 using the default configuration of the Word alignment precision when the threshold is chosen to achieve IBM Model 4 recall with a difference of ± 0.005. The average relative increase in precision (against the HMM model) is 10% for IBM Model 4, 11% for B-HMM, and 14% for S-HMM. MOSES training script.3 This performs five iterations of IBM Model 1, five iterations of HMM, and five iterations of IBM Model 4.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:326
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Cross-lingual Textual Entailment (CLTE) has been proposed by (Mehdad et al., 2010) as an extension of Textual Entailment (Dagan and Glickman, 2004) that consists in deciding, given two texts T and H in different languages, if the meaning of H can be inferred from the meaning of T.
Citation Sentence:
Cross-lingual Textual Entailment ( CLTE ) has been proposed by ( Mehdad et al. , 2010 ) as an extension of Textual Entailment ( Dagan and Glickman , 2004 ) that consists in deciding , given two texts T and H in different languages , if the meaning of H can be inferred from the meaning of T .
Context after the citation:
The task is inherently difficult, as it adds issues related to the multilingual dimension to the complexity of semantic inference at the textual level. For instance, the reliance of current monolingual TE systems on lexical resources (e.g. WordNet, VerbOcean, FrameNet) and deep processing components (e.g. syntactic and semantic parsers, co-reference resolution tools, temporal expressions recognizers and normalizers) has to confront, at the cross-lingual level, with the limited availability of lexical/semantic resources covering multiple languages, the limited coverage of the existing ones, and the burden of integrating languagespecific components into the same cross-lingual architecture. As a first step to overcome these problems, (Mehdad et al., 2010) proposes a âbasic solutionâ, that brings CLTE back to the monolingual scenario by translating H into the language of T. Despite the advantages in terms of modularity and portability of the architecture, and the promising experimental results, this approach suffers from one main limitation which motivates the investigation on alternative solutions. Decoupling machine translation (MT) and TE, in fact, ties CLTE performance to the availability of MT components, and to the quality of the translations.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:327
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Some approaches apply semantic parsing, where words and sentences are mapped to logical structure meaning (Kate and Mooney, 2007). The language grounding problem has come in many different flavors with just as many different approaches. The underlying hypothesis is that the meanings of words are explicitly tied to our perception and understanding of the world around us, and textual-information alone is insufficient for a complete understanding of language.
Citation Sentence:
Some approaches apply semantic parsing , where words and sentences are mapped to logical structure meaning ( Kate and Mooney , 2007 ) .
Context after the citation:
Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012). Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the âmeaning of words is entirely given by other wordsâ (Bruni et al., 2012b).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:328
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
conclusion
Context before the citation:
For projective parsing, it is significantly faster than exact dynamic programming, at the cost of small amounts of search error, We are interested in extending these ideas to phrase-structure and lattice parsing, and in trying other higher-order features, such as those used in parse reranking (Charniak and Johnson, 2005; Huang, 2008) and history-based parsing (Nivre and McDonald, 2008). Belief propagation improves non-projective dependency parsing with features that would make exact inference intractable.
Citation Sentence:
For projective parsing , it is significantly faster than exact dynamic programming , at the cost of small amounts of search error , We are interested in extending these ideas to phrase-structure and lattice parsing , and in trying other higher-order features , such as those used in parse reranking ( Charniak and Johnson , 2005 ; Huang , 2008 ) and history-based parsing ( Nivre and McDonald , 2008 ) .
Context after the citation:
We could also introduce new variables, e.g., nonterminal refinements (Matsuzaki et al., 2005), or secondary links Mid (not constrained by TREE/PTREE) that augment the parse with representations of control, binding, etc. (Sleator and Temperley, 1993; Buch-Kromann, 2006). Other parsing-like problems that could be attacked with BP appear in syntax-based machine translation. Decoding is very expensive with a synchronous grammar composed with an n-gram language model (Chiang, 2007)âbut our footnote 10 suggests that BP might incorporate a language model rapidly. String alignment with synchronous grammars is quite expensive even for simple synchronous formalisms like ITG (Wu, 1997)âbut Duchi et al. (2007) show how to incorporate bipartite matching into max-product BP.
|
FutureWork
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:329
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
We are going to make such a comparison with the theories proposed by J. Hobbs (1979, 1982) that represent a more computationally oriented approach to coherence, and those of T.A. van Dijk and W. Kintch (1983), who are more interested in addressing psychological and cognitive aspects of discourse coherence. At this point it may be proper to comment on the relationship between our theory of coherence and theories advocated by others.
Citation Sentence:
We are going to make such a comparison with the theories proposed by J. Hobbs ( 1979 , 1982 ) that represent a more computationally oriented approach to coherence , and those of T.A. van Dijk and W. Kintch ( 1983 ) , who are more interested in addressing psychological and cognitive aspects of discourse coherence .
Context after the citation:
The quoted works seem to be good representatives for each of the directions; they also point to related literature. The approach we advocate is compatible with the work of these researchers, we believe. There are, however, some interesting differences: first of all, we emphasize the role of paragraphs; second, we talk about formal principles regulating the organization and use of knowledge in language understanding; and third, we realize that natural language text (such as an on-line dictionary) can, in many cases, provide the type of commonsense background information that Hobbs (for example) advocated but didn't know how to access. (There are also some other, minor, differences.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:33
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguistâs Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003). Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler.
Citation Sentence:
Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) .
Context after the citation:
A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies. Word frequency counts in internet search engines are inconsistent and unreliable (Veronis, 2005). Tools based on static corpora do not suffer from this problem, e.g. BNCweb7, developed at the University of Zurich, and View 8 (Variation in English Words and Phrases, developed at Brigham Young University) 4 http://www.comp.lancs.ac.uk/ucrel/claws/trial.html 5 http://www.comp.leeds.ac.uk/amalgam/amalgam/ amalghome.htm 6 http://www.connexor.com 7 http://homepage.mac.com/bncweb/home.html 8 http://view.byu.edu/
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:330
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary, i.e., mouse Mouse Genome Database (MGD) [18], fly FlyBase [19], yeast Saccharomyces Genome Database (SGD) [20], rat â Rat Genome Database (RGD) [21], worm â WormBase [22], Human Nomenclature Database (HUGO) [23], Online Mendelian Inheritance in Man (OMIM) [24], and Enzyme Nomenclature Database (ECNUM) [25, 26]. The Semantic Network contains information about the types or categories (e.g., âDisease or Syndromeâ, âVirusâ) to which all META concepts have been assigned. The SPECIALIST lexicon contains syntactic information for many terms, component words, and English words, including verbs, which do not appear in the META.
Citation Sentence:
Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] .
Context after the citation:
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:331
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Other factors, such as the role of focus (Grosz 1977, 1978; Sidner 1983) or quantifier scoping (Webber 1983) must play a role, too. They are intended as an illustration of the power of abduction, which in this framework helps determine the universe of the model (that is the set of entities that appear in it). We have no doubts that various other metarules will be necessary; clearly, our two metarules cannot constitute the whole theory of anaphora resolution.
Citation Sentence:
Other factors , such as the role of focus ( Grosz 1977 , 1978 ; Sidner 1983 ) or quantifier scoping ( Webber 1983 ) must play a role , too .
Context after the citation:
Determining the relative importance of those factors, the above metarules, and syntactic clues, appears to be an interesting topic in itself. Note: In our translation from English to logic we are assuming that "it" is anaphoric (with the pronoun following the element that it refers to), not cataphoric (the other way around). This means that the "it" that brought the disease in P1 will not be considered to refer to the infection "i" or the death "d" in P3. This strategy is certainly the right one to start out with, since anaphora is always the more typical direction of reference in English prose (Halliday and Hasan 1976, p. 329).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:332
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Since mid-2002, the Library has been employing software that automatically suggests MeSH headings based on content (Aronson et al. 2004). Indexing is performed by approximately 100 indexers with at least bachelorâs degrees in life sciences and formal training in indexing provided by NLM. separate thesaurus.
Citation Sentence:
Since mid-2002 , the Library has been employing software that automatically suggests MeSH headings based on content ( Aronson et al. 2004 ) .
Context after the citation:
Nevertheless, the indexing process remains firmly human-centered. As a concrete example, an abstract titled âAntipyretic efficacy of ibuprofen vs. acetaminophenâ might have the following MeSH headings associated with it: To represent different aspects of the topic described by a particular MeSH heading, up to three subheadings may be assigned, as indicated by the slash notation. In this example, a trained user could interpret from the MeSH terms that the article is about drug therapy for fever and the therapeutic use of ibuprofen and acetaminophen.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:333
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Discriminative approaches (especially SVMs) have been shown to be very effective for many supervised classification tasks; see, for example, (Joachims, 1998; Ng and Jordan, 2001). Although our results were not obtained from the same exact collection as those used by authors of these two previous studies, comparable experiments suggest that our techniques are competitive in terms of performance, and may offer additional advantages as well. Building on the work of Ruch et al. (2003) in the same domain, we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models (HMMs); cfXXX (Barzilay and Lee, 2004).
Citation Sentence:
Discriminative approaches ( especially SVMs ) have been shown to be very effective for many supervised classification tasks ; see , for example , ( Joachims , 1998 ; Ng and Jordan , 2001 ) .
Context after the citation:
However, their high computational complexity (quadratic in the number of training samples) renders them prohibitive for massive data processing. Under certain conditions, generative approaches with linear complexity are preferable, even if their performance is lower than that which can be achieved through discriminative training. Since HMMs are very wellsuited to modeling sequences, our discourse modeling task lends itself naturally to this particular generative approach. In fact, we demonstrate that HMMs are competitive with SVMs, with the added advantage of lower computational complexity.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:334
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Also, the Keller and Lapata (2003) approach will be undefined if the pair is unobserved on the web. The similarity-smoothed examples will be undefined if SIMS(w) is empty. not be able to provide a score for each example.
Citation Sentence:
Also , the Keller and Lapata ( 2003 ) approach will be undefined if the pair is unobserved on the web .
Context after the citation:
As a reasonable default for these cases, we assign them a negative decision. We evaluate disambiguation using precision (P), recall (R), and their harmonic mean, F-Score (F). Table 1 gives the results of our comparison. In the MacroAvg results, we weight each example equally.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:335
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
The automation of help-desk responses has been previously tackled using mainly knowledge-intensive paradigms, such as expert systems (Barr and Tessler 1995) and case-based reasoning (Watson 1997).
Citation Sentence:
The automation of help-desk responses has been previously tackled using mainly knowledge-intensive paradigms , such as expert systems ( Barr and Tessler 1995 ) and case-based reasoning ( Watson 1997 ) .
Context after the citation:
Such technologies require significant human input, and are difficult to create and maintain (Delic and Lahaix 1998). In contrast, the techniques examined in this article are corpus-based and data-driven. The process of composing a planned response for a new request is informed by probabilistic and lexical properties of the requests and responses in the corpus. There are very few reported attempts at corpus-based automation of help-desk responses (Carmel, Shtalhaim, and Soffer 2000; Lapalme and Kosseim 2003; Bickel and Scheffer 2004; Malik, Subramaniam, and Kaushik 2007).
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:336
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
We measure this association using pointwise Mutual Information (MI) (Church and Hanks, 1990). To create the positives, we automatically parse a large corpus, and then extract the predicate-argument pairs that have a statistical association in this data. To learn a discriminative model of selectional preference, we create positive and negative training examples automatically from raw text.
Citation Sentence:
We measure this association using pointwise Mutual Information ( MI ) ( Church and Hanks , 1990 ) .
Context after the citation:
The MI between a verb predicate, v, and its object argument, n, is: If MI>0, the probability v and n occur together is greater than if they were independently distributed. We create sets of positive and negative examples separately for each predicate, v. First, we extract all pairs where MI(v, n)>T as positives.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:337
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts (Baker et al., 1998).
Citation Sentence:
Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts ( Baker et al. , 1998 ) .
Context after the citation:
In other methods, lexical resources are specifically tailored to meet the requirements of the domain (Rosario and Hearst, 2001) or the system (Gomez, 1998). Such systems extract information from some types of syntactic units (clauses in (Fillmore and Atkins, 1998; Gildea and Jurafsky, 2002; Hull and Gomez, 1996); noun phrases in (Hull and Gomez, 1996; Rosario et al., 2002)). Lists of semantic relations are designed to capture salient domain information. In the Rapid Knowledge Formation Project (RKF) a support system was developed for domain experts.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:338
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
There are two corpora normally used for evaluation in a number of text-processing tasks: the Brown corpus (Francis and Kucera 1982) and the Wall Street Journal (WSJ) corpus, both part of the Penn Treebank (Marcus, Marcinkiewicz, and Santorini 1993). 2.1 Corpora for Evaluation In this case apart from the error rate (which corresponds to precision or accuracy as 1âerror rate) we also measure the systemâs coverage or recall
Citation Sentence:
There are two corpora normally used for evaluation in a number of text-processing tasks : the Brown corpus ( Francis and Kucera 1982 ) and the Wall Street Journal ( WSJ ) corpus , both part of the Penn Treebank ( Marcus , Marcinkiewicz , and Santorini 1993 ) .
Context after the citation:
The Brown corpus represents general English. It contains over one million word tokens and is composed from 15 subcorpora that belong to different genres and domains, ranging from news wire texts and scientific papers to fiction and transcribed speech. The Brown corpus is rich in out-of-vocabulary (unknown) words, spelling errors, and ungrammatical sentences with complex internal structure. Altogether there are about 500 documents in the Brown corpus, with an average length of 2,300 word tokens.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:339
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
11 From (Zollmann and Vogel, 2011), we find that the performance of SAMT system is similar with the method of labeling SCFG rules with POS tags. We only use one sample to extract the translation grammar because multiple samples would result in a grammar that would be too large. We built an s2t translation system with the achieved U-trees after the 1000th iteration.
Citation Sentence:
11 From ( Zollmann and Vogel , 2011 ) , we find that the performance of SAMT system is similar with the method of labeling SCFG rules with POS tags .
Context after the citation:
Thus, to be convenient, we only conduct experiments with the SAMT system.
|
Motivation
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:34
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
or quotation of messages in emails or postings (see Mullen and Malouf (2006) but cfXXX Agrawal et al. (2003)). tween two speakers, such as explicit assertions (âI second that!â) Indeed, in other settings (e.g., a movie-discussion listserv) one may not be able to determine the participantsâ political leanings, and such information may not lead to significantly improved results even if it were available.
Citation Sentence:
or quotation of messages in emails or postings ( see Mullen and Malouf ( 2006 ) but cfXXX Agrawal et al. ( 2003 ) ) .
Context after the citation:
Agreement evidence can be a powerful aid in our classification task: for example, we can easily categorize a complicated (or overly terse) document if we find within it indications of agreement with a clearly positive text. Obviously, incorporating agreement information provides additional benefit only when the input documents are relatively difficult to classify individually. Intuition suggests that this is true of the data with which we experiment, for several reasons. First, U.S. congressional debates contain very rich language and cover an extremely wide variety of topics, ranging from flag burning to international policy to the federal budget.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:340
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
Andrews et al. (2009) extend LDA to allow for the inference of document and topic distributions in a multimodal corpus.
Citation Sentence:
Andrews et al. ( 2009 ) extend LDA to allow for the inference of document and topic distributions in a multimodal corpus .
Context after the citation:
In their model, a document consists of a set of (word, feature) pairs,4 rather than just words, and documents are still modeled as mixtures of shared topics. Topics consist of multinomial distributions over words, βk, but are extended to also include multinomial distributions over features, Ïk. The generative process is amended to include these feature distributions: 1.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:341
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
SNoW (Carleson et al., 1999; Roth, 1998) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources (features) taking part in decisions is very large, of which NLP is a principal example. The shallow parser used is the SNoW-based CSCL parser (Punyakanok and Roth, 2001; Munoz et al., 1999). The reported results for the full parse tree (on section 23) are recall/precision of 88.1/87.5 (Collins, 1997).
Citation Sentence:
SNoW ( Carleson et al. , 1999 ; Roth , 1998 ) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources ( features ) taking part in decisions is very large , of which NLP is a principal example .
Context after the citation:
It works by learning a sparse network of linear functions over a pre-defined or incrementally learned feature space. Typically, SNoW is used as a classifier, and predicts using a winner-take-all mechanism over the activation value of the target classes. However, in addition to the prediction, it provides a reliable confidence level in the prediction, which enables its use in an inference algorithm that combines predictors to produce a coherent inference. Indeed, in CSCL (constraint satisfaction with classifiers), SNoW is used to learn several different classifiers â each detects the beginning or end of a phrase of some type (noun phrase, verb phrase, etc.).
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:342
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
The search algorithm is the standard Viterbi search (Viterbi 1967), except that the match involves a network-to-network alignment problem rather than sequence-to-sequence. The lexicon is entered as phonetic pronunciations that are then augmented to account for a number of phonological rules. The recognizer for these systems is the SUMMIT system (Zue et al. 1989), which uses a segmental-based framework and includes an auditory model in the front-end processing.
Citation Sentence:
The search algorithm is the standard Viterbi search ( Viterbi 1967 ) , except that the match involves a network-to-network alignment problem rather than sequence-to-sequence .
Context after the citation:
When we first integrated this recognizer with TINA, we used a "wire" connection, in that the recognizer produced a single best output, which was then passed to TINA for parsing. A simple word-pair grammar constrained the search space. If the parse failed, then the sentence was rejected. We have since improved the interface by incorporating a capability in the recognizer to propose additional solutions in turn once the first one fails to parse (Zue et al. 1991) To produce these "N-best" alternatives, we make use of a standard A* search algorithm (Hart 1968, Jelinek 1976).
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:343
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Since then this idea has been applied to several tasks, including word sense disambiguation (Yarowsky 1995) and named-entity recognition (Cucerzan and Yarowsky 1999). Gale, Church, and Yarowsky (1992) showed that words strongly tend to exhibit only one sense in a document or discourse (âone sense per discourseâ). It has been applied not only to the identification of proper names, as described in this article, but also to their classification (Mikheev, Grover, and Moens 1998).
Citation Sentence:
Since then this idea has been applied to several tasks , including word sense disambiguation ( Yarowsky 1995 ) and named-entity recognition ( Cucerzan and Yarowsky 1999 ) .
Context after the citation:
Gale, Church, and Yarowskyâs observation is also used in our DCA, especially for the identification of abbreviations. In capitalized-word disambiguation, however, we use this assumption with caution and first apply strategies that rely not just on single words but on words together with their local contexts (n-grams). This is similar to âone sense per collocationâ idea of Yarowsky (1993). The description of the EAGLE workbench for linguistic engineering (Baldwin et al. 1997) mentions a case normalization module that uses a heuristic in which a capitalized word in an ambiguous position should be rewritten without capitalization if it is found lower-cased in the same document.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:344
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
7 We employed the LIBSVM package (Chang and Lin 2001). 6 For Sent-Pred we also experimented with grammatical and sentence-based syntactic features, such as number of syntactic phrases, grammatical mood, and grammatical person (Marom and Zukerman 2006), but the simple binary bag-of-lemmas representation yielded similar results. During the
Citation Sentence:
7 We employed the LIBSVM package ( Chang and Lin 2001 ) .
Context after the citation:
1. Calculate the scores of the sentences in the predicted SCs. 2. Remove redundant sentences from cohesive SCs; these are SCs which contain similar sentences.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:345
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
coreference performance on perfect mentions (e.g., Incorporate the two knowledge sources in a Luo et al. (2004)); and for those that do report percoreference resolver. This KS could do not evaluate the role of induced SC knowledge be useful for ACE coreference, since ACE is conin coreference resolution: many of them evaluate cerned with resolving only NPs that are mentions. More importantly, the ACE participants SC is OTHERS, and YES otherwise.
Citation Sentence:
coreference performance on perfect mentions ( e.g. , Incorporate the two knowledge sources in a Luo et al. ( 2004 ) ) ; and for those that do report percoreference resolver .
Context after the citation:
Next, we investigate whether formance on automatically extracted mentions, they these two KSs can improve a learning-based basedo not explain whether or how the induced SC inforline resolver that employs a fairly standard feature mation is used in their coreference algorithms. set. Since (1) the two KSs can each be repreJoint probabilistic models of coreference. Resented in the resolver as a constraint (for filtering cently, there has been a surge of interest in imnon-mentions or disallowing coreference between proving coreference resolution by jointly modeling semantically incompatible NPs) or as a feature, and coreference with a related task such as MD (e.g., (2) they can be applied to the resolver in isolation or Daum´e and Marcu (2005)).
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:346
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
According to Hobbs (1979, p. 67), these two sentences are incoherent. He likes spinach. To see the advantage of assuming that coherence is a property of a fragment of a text/discourse, and not a relation between subsequent sentences, let us consider for instance the text John took a train from Paris to Istanbul.
Citation Sentence:
According to Hobbs ( 1979 , p. 67 ) , these two sentences are incoherent .
Context after the citation:
However, the same fragment, augmented with the third sentence Mary told him yesterday that the French spinach crop failed and Turkey is the only country. . . (ibid.) suddenly (for Hobbs) becomes coherent. It seems that any analysis of coherence in terms of the relation between subsequent sentences cannot explain this sudden change; after all, the first two sentences didn't change when the third one was added. On the other hand, this change is easily explained when we treat the first two sentences as a paragraph: if the third sentence is not a part of the background knowledge, the paragraph is incoherent.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:347
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000). While earlier work in this direction concentrated on manual construction of rules, most of the recent work has been motivated by the observation that shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part-of-speech information. to ] [NP only $ 1.8 billion ] [PP in ] [NP September] .
Citation Sentence:
Thus , over the past few years , along with advances in the use of learning and statistical methods for acquisition of full parsers ( Collins , 1997 ; Charniak , 1997a ; Charniak , 1997b ; Ratnaparkhi , 1997 ) , significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship ( Church , 1988 ; Ramshaw and Marcus , 1995 ; Argamon et al. , 1998 ; Cardie and Pierce , 1998 ; Munoz et al. , 1999 ; Punyakanok and Roth , 2001 ; Buchholz et al. , 1999 ; Tjong Kim Sang and Buchholz , 2000 ) .
Context after the citation:
Research on shallow parsing was inspired by psycholinguistics arguments (Gee and Grosjean, 1983) that suggest that in many scenarios (e.g., conversational) full parsing is not a realistic strategy for sentence processing and analysis, and was further motivated by several arguments from a natural language engineering viewpoint. First, it has been noted that in many natural language applications it is sufficient to use shallow parsing information; information such as noun phrases (NPs) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization (Grishman, 1995; Appelt et al., 1993). Second, while training a full parser requires a collection of fully parsed sentences as training corpus, it is possible to train a shallow parser incrementally. If all that is available is a collection of sentences annotated for NPs, it can be used to produce this level of analysis.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:348
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Another dialogue acquisition system has been developed by Ho (1984). However, the "flowcharts" in the current project are probabilistic in nature and the problems associated with matching incoming sentences to existing nodes has not been previously addressed. The acquisition of dialogue as implemented in VNLCE is reminiscent of the program synthesis methodology developed by Biermann and Krishnaswamy (1976) where program flowcharts were constructed from traces of their behaviors.
Citation Sentence:
Another dialogue acquisition system has been developed by Ho ( 1984 ) .
Context after the citation:
However, that system has different goals: to enable the user to consciously design a dialogue to embody a particular human-machine interaction. The acquisition system described here is aimed at dealing with ill-formed input and is completely automatic and invisible to the user. It self activates to bias recognition toward historically observed patterns but is not otherwise observable. The VNLCE processor may be considered to be a learning system of the tradition described, for example, in Michalski et al. (1984).
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:349
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
In this paper we focus on the exploitation of the LDOCE grammar coding system; Alshawi et al. (1985) and Alshawi (1987) describe further research in Cambridge utilising different types of information available in LDOCE. (Michiels (1982) contains further description and discussion of LDOCE.) Most prominent among these are the rich grammatical subcategorisations of the 60,000 entries, the large amount of information concerning phrasal verbs, noun compounds and idioms, the individual subject, collocational and semantic codes for the entries and the consistent use of a controlled 'core' vocabulary in defining the words throughout the dictionary.
Citation Sentence:
In this paper we focus on the exploitation of the LDOCE grammar coding system ; Alshawi et al. ( 1985 ) and Alshawi ( 1987 ) describe further research in Cambridge utilising different types of information available in LDOCE .
Context after the citation:
The information available in the dictionary is both very rich and diverse, but also typically only semiformalised, as it is intended for human, rather than machine, interpetation. As a consequence the programs we are developing, both to restructure and to exploit this information, need to undergo constant revision as they are being used. The system we describe is not intended for off-line use, where one might attempt to derive, completely automatically, a lexicon for natural language analysis. Rather than trying to batch process the electronic source, lexicon development from the LDOCE tape is more incremental and interactive.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:35
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Lexical functional grammar (Kaplan and Bresnan 1982; Bresnan 2001; Dalrymple 2001) is a member of the family of constraint-based grammars.
Citation Sentence:
Lexical functional grammar ( Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ) is a member of the family of constraint-based grammars .
Context after the citation:
It posits minimally two levels of syntactic representation:2 c(onstituent)-structure encodes details of surface syntactic constituency, whereas f(unctional)-structure expresses abstract syntactic information about predicateâargumentâmodifier relations and certain morphosyntactic properties such as tense, aspect, and case. C-structure takes the form of phrase structure trees and is defined in terms of CFG rules and lexical entries. F-structure is produced from functional annotations on the nodes of the c-structure and implemented in terms of recursive feature structures (attributeâvalue matrices). This is exemplified by the analysis of the string The inquiry soon focused on the judge (wsj 0267 72) using the grammar in Figure 1, which results in the annotated c-structure and f-structure in Figure 2.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:350
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
1Our rules are similar to those from Xu et al. (2009). The reordering cost, evaluation We evaluate our results on an evaluation set of 6338 examples of similarly created reordering data.
Citation Sentence:
1Our rules are similar to those from Xu et al. ( 2009 ) .
Context after the citation:
criteria and data used in our experiments are based on the work of Talbot et al. (2011). Table 1 shows the results of using the reordering cost as an augmented-loss to the standard treebank objective function. Results are presented as measured by the reordering score as well as a coarse exact-match score (the number of sentences which would have correct word-order given the parse and the fixed reordering rules). We see continued improvements as we adjust the schedule to process the extrinsic loss more frequently, the best result being when we make two augmented-loss updates for every one treebank-based loss update.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:351
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
In psycholinguistics, relatedness of words can also be determined through association tests (Schulte im Walde and Melinger, 2005). They used a 0-10 range of relatedness scores, but did not give further details about their experimental setup. Finkelstein et al. (2002) annotated a larger set of word pairs (353), too.
Citation Sentence:
In psycholinguistics , relatedness of words can also be determined through association tests ( Schulte im Walde and Melinger , 2005 ) .
Context after the citation:
Results of such experiments are hard to quantify and cannot easily serve as the basis for evaluating SR measures. Rubenstein and Goodenough selected word pairs analytically to cover the whole spectrum of similarity from ânot similarâ to âsynonymousâ. This elaborate process is not feasible for a larger dataset or if domain-specific test sets should be compiled quickly.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:352
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
In this article, we use an in-house system which provides functional gender, number, and rationality features (Alkuhlani and Habash 2012). functional inflectional number feature, but not full functional gender (only for adjectives and verbs but not for nouns), nor rationality. 6 Note that the functional and form-based feature values for verbs always coincide.
Citation Sentence:
In this article , we use an in-house system which provides functional gender , number , and rationality features ( Alkuhlani and Habash 2012 ) .
Context after the citation:
See Section 5.2 for more details.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:353
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations (Jordan et al., 2006; Graesser et al., 1999; Aleven et al., 2001; Buckley and Wolska, 2007; Nielsen et al., 2008; VanLehn et al., 2007), because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring (Chi et al., 1994; Litman et al., 2009; Purandare and Litman, 2008; Steinhauser et al., 2007).
Citation Sentence:
Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations ( Jordan et al. , 2006 ; Graesser et al. , 1999 ; Aleven et al. , 2001 ; Buckley and Wolska , 2007 ; Nielsen et al. , 2008 ; VanLehn et al. , 2007 ) , because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring ( Chi et al. , 1994 ; Litman et al. , 2009 ; Purandare and Litman , 2008 ; Steinhauser et al. , 2007 ) .
Context after the citation:
However, most existing systems use pre-authored tutor responses for addressing student errors. The advantage of this approach is that tutors can devise remediation dialogues that are highly tailored to specific misconceptions many students share, providing step-by-step scaffolding and potentially suggesting additional problems. The disadvantage is a lack of adaptivity and generality: students often get the same remediation for the same error regardless of their past performance or dialogue context, as it is infeasible to author a different remediation dialogue for every possible dialogue state.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:354
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
The use of the web as a corpus for teaching and research on language has been proposed a number of times (Kilgarriff, 2001; Robb, 2003; Rundell, 2000; Fletcher, 2001, 2004b) and received a special issue of the journal Computational Linguistics (Kilgarriff and Grefenstette, 2003). This corpus annotation bottleneck becomes even more problematic for voluminous data sets drawn from the web. Larger systems to support multiple document tagging processes would require resources that cannot be realistically provided by existing single-server systems.
Citation Sentence:
The use of the web as a corpus for teaching and research on language has been proposed a number of times ( Kilgarriff , 2001 ; Robb , 2003 ; Rundell , 2000 ; Fletcher , 2001 , 2004b ) and received a special issue of the journal Computational Linguistics ( Kilgarriff and Grefenstette , 2003 ) .
Context after the citation:
Studies have used several different methods to mine web data. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguistâs Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:355
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
Several authors in communication studies have pointed out that head movements are relevant to feedback phenomena (see McClave (2000) for an overview).
Citation Sentence:
Several authors in communication studies have pointed out that head movements are relevant to feedback phenomena ( see McClave ( 2000 ) for an overview ) .
Context after the citation:
Others have looked at the application of machine learning algorithms to annotated multimodal corpora. For example, Jokinen and Ragni (2007) and Jokinen et al. (2008) find that machine learning algorithms can be trained to recognise some of the functions of head movements, while Reidsma et al. (2009) show that there is a dependence between focus of attention and assignment of dialogue act labels. Related are also the studies by Rieks op den Akker and Schulz (2008) and Murray and Renals (2008): both achieve promising results in the automatic segmentation of dialogue acts using the annotations in a large multimodal corpus. Work has also been done on prosody and gestures in the specific domain of map-task dialogues, also targeted in this paper.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:356
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Our method resorts to some translation examples, which is similar as example-based translation or translation memory (Watanabe and Sumita, 2003; He et al., 2010; Ma et al., 2011). One of the advantages is that it can adapt the weights for each of the test sentences. Further, our translation framework integrates the training and testing into one unit, instead of treating them separately.
Citation Sentence:
Our method resorts to some translation examples , which is similar as example-based translation or translation memory ( Watanabe and Sumita , 2003 ; He et al. , 2010 ; Ma et al. , 2011 ) .
Context after the citation:
Instead of using translation examples to construct translation rules for enlarging the decoding space, we employed them to discriminatively learn local weights. Similar to (Hildebrand et al., 2005; L¨u et al., 2007), our method also employes IR methods to retrieve examples for a given test set. Their methods utilize the retrieved examples to acquire translation model and can be seen as the adaptation of translation model. However, ours uses the retrieved examples to tune the weights and thus can be considered as the adaptation of tuning.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:357
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Baseline Systems We choose three publicly available state-of-the-art end-to-end coreference systems as our baselines: Stanford system (Lee et al., 2011), Berkeley system (Durrett and Klein, 2014) and HOTCoref system (Bj¨orkelund and Kuhn, 2014). 3.1.1 can be verified empirically on both ACE-2004 and OntoNotes-5.0 datasets. The nonoverlapping mention head assumption in Sec.
Citation Sentence:
Baseline Systems We choose three publicly available state-of-the-art end-to-end coreference systems as our baselines : Stanford system ( Lee et al. , 2011 ) , Berkeley system ( Durrett and Klein , 2014 ) and HOTCoref system ( Bj ¨ orkelund and Kuhn , 2014 ) .
Context after the citation:
Developed Systems Our developed system is built on the work by Chang et al. (2013), using Constrained Latent Left-Linking Model (CL3M) as our mention-pair coreference model in the joint framework10. When the CL3M coreference system uses gold mentions or heads, we call the system Gold; when it uses predicted mentions or heads, we call the system Predicted. The mention head candidate generation module along with mention boundary detection module can be grouped together to form a complete mention detection system, and we call it H-M-MD. We can feed the predicted mentions from H-M-MD directly into the mention-pair coref-
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:358
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
A number of speech understanding systems have been developed during the past fifteen years (Barnett et al. 1980, Dixon and Martin 1979, Erman et al. 1980, Haton and Pierrel 1976, Lea 1980, Lowerre and Reddy 1980, Medress 1980, Reddy 1976, Walker 1978, and Wolf and Woods 1980).
Citation Sentence:
A number of speech understanding systems have been developed during the past fifteen years ( Barnett et al. 1980 , Dixon and Martin 1979 , Erman et al. 1980 , Haton and Pierrel 1976 , Lea 1980 , Lowerre and Reddy 1980 , Medress 1980 , Reddy 1976 , Walker 1978 , and Wolf and Woods 1980 ) .
Context after the citation:
Most of these efforts concentrated on the interaction between low level information sources from a speech recognizer and a natural language processor to discover the meaning of an input sentence. While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983).
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:359
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
Gurevych (2006) observed that some annotators were not familiar with the exact definition of semantic relatedness. Thus, they were not supervised during the experiment. Test subjects were invited via email to participate in the experiment.
Citation Sentence:
Gurevych ( 2006 ) observed that some annotators were not familiar with the exact definition of semantic relatedness .
Context after the citation:
Their results differed particularly in cases of antonymy or distributionally related pairs. We created a manual with a detailed introduction to SR stressing the crucial points. The manual was presented to the subjects before the experiment and could be re-accessed at any time. During the experiment, one concept pair at a time was presented to the test subjects in random ordering.
|
Motivation
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:36
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
As for work on Arabic (MSA), results have been reported on the PATB (Kulick, Gabbard, and Marcus 2006; Diab 2007; Green and Manning 2010), the Prague Dependency Treebank (PADT) (Buchholz and Marsi 2006; Nivre 2008) and the CATiB (Habash and Roth 2009). Looking at Hebrew, a Semitic language related to Arabic, Tsarfaty and Simaâan (2007) report that extending POS and phrase structure tags with definiteness information helps unlexicalized PCFG parsing. We also find that the number feature helps for Arabic.
Citation Sentence:
As for work on Arabic ( MSA ) , results have been reported on the PATB ( Kulick , Gabbard , and Marcus 2006 ; Diab 2007 ; Green and Manning 2010 ) , the Prague Dependency Treebank ( PADT ) ( Buchholz and Marsi 2006 ; Nivre 2008 ) and the CATiB ( Habash and Roth 2009 ) .
Context after the citation:
Recently, Green and Manning (2010) analyzed the PATB for annotation consistency, and introduced an enhanced split-state constituency grammar, including labels for short idafa constructions and verbal or equational clauses. Nivre (2008) reports experiments on Arabic parsing using his MaltParser (Nivre et al. 2007), trained on the PADT. His results are not directly comparable to ours because of the different treebank representations, even though all the experiments reported here were performed using the MaltParser. Our results agree with previous work on Arabic and Hebrew in that marking the definite article is helpful for parsing.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:360
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Other representations use the link structure (Malin, 2005) or generate graph representations of the extracted features (Kalashnikov et al., 2007). Nevertheless, the full document text is present in most systems, sometimes as the only feature (Sugiyama and Okumura, 2007) and sometimes in combination with others see for instance (Chen and Martin, 2007; Popescu and Magnini, 2007)-. Withindocument coreference resolution has been applied to produce summaries of text surrounding occurrences of the name (Bagga and Baldwin, 1998; Gooi and Allan, 2004).
Citation Sentence:
Other representations use the link structure ( Malin , 2005 ) or generate graph representations of the extracted features ( Kalashnikov et al. , 2007 ) .
Context after the citation:
Some researchers (Cucerzan, 2007; Nguyen and Cao, 2008) have explored the use of Wikipedia information to improve the disambiguation process. Wikipedia provides candidate entities that are linked to specific mentions in a text. The obvious limitation of this approach is that only celebrities and historical figures can be identified in this way. These approaches are yet to be applied to the specific task of grouping search results.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:361
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
From an IR view, a lot of specialized research has already been carried out for medical applications, with emphasis on the lexico-semantic aspects of dederivation and decomposition (Pacak et al., 1980; Norton and Pacak, 1983; Wolff, 1984; Wingert, 1985; Dujols et al., 1991; Baud et al., 1998). This is particularly true for the medical domain. When it comes to a broader scope of morphological analysis, including derivation and composition, even for the English language only restricted, domain-specific algorithms exist.
Citation Sentence:
From an IR view , a lot of specialized research has already been carried out for medical applications , with emphasis on the lexico-semantic aspects of dederivation and decomposition ( Pacak et al. , 1980 ; Norton and Pacak , 1983 ; Wolff , 1984 ; Wingert , 1985 ; Dujols et al. , 1991 ; Baud et al. , 1998 ) .
Context after the citation:
While one may argue that single-word compounds are quite rare in English (which is not the case in the medical domain either), this is certainly not true for German and other basically agglutinative languages known for excessive single-word nominal compounding. This problem becomes even more pressing for technical sublanguages, such as medical German (e.g., âBlut druck mess gerdtâ translates to âdevice for measuring blood pressureâ). The problem one faces from an IR point of view is that besides fairly standardized nominal compounds, which already form a regular part of the sublanguage proper, a myriad of ad hoc compounds are formed on the fly which cannot be anticipated when formulating a retrieval query though they appear in relevant documents. Hence, enumerating morphological variants in a semi-automatically generated lexicon, such as proposed for French (Zweigenbaum et al., 2001), turns out to be infeasible, at least for German and related languages.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:362
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
conclusion
Context before the citation:
There has been some controversy, at least for simple stemmers (Lovins, 1968; Porter, 1980), about the effectiveness of morphological analysis for document retrieval (Harman, 1991; Krovetz, 1993; Hull, 1996).
Citation Sentence:
There has been some controversy , at least for simple stemmers ( Lovins , 1968 ; Porter , 1980 ) , about the effectiveness of morphological analysis for document retrieval ( Harman , 1991 ; Krovetz , 1993 ; Hull , 1996 ) .
Context after the citation:
The key for quality improvement seems to be rooted mainly in the presence or absence of some form of dictionary. Empirical evidence has been brought forward that inflectional and/or derivational stemmers augmented by dictionaries indeed perform substantially better than those without access to such lexical repositories (Krovetz, 1993; Kraaij and Pohlmann, 1996; Tzoukermann et al., 1997). This result is particularly valid for natural languages with a rich morphology â both in terms of derivation and (single-word) composition. Document retrieval in these languages suffers from serious performance degradation with the stemmingonly query-term-to-text-word matching paradigm.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:363
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
conclusion
Context before the citation:
This revalidates the observation of Nguyen et al. (2009) that phrase structure representations and dependency representations add complimentary value to the learning task. Furthermore, because of the complexity of the task, a combination of phrase based structures and dependency-based structures perform the best. Our experiments show that as a result of how language expresses the relevant information, dependency-based structures are best suited for encoding this information.
Citation Sentence:
This revalidates the observation of Nguyen et al. ( 2009 ) that phrase structure representations and dependency representations add complimentary value to the learning task .
Context after the citation:
We also introduced a new sequence structure (SqGRW) which plays a role in achieving the best accuracy for both, social event detection and social event classification tasks. In the future, we will use other parsers (such as semantic parsers) and explore new types of linguistically motivated structures and transformations. We will also investigate the relation between classes of social events and their syntactic realization.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:364
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
The parsing algorithm used for all languages is the deterministic algorithm first proposed for unlabeled dependency parsing by Nivre (2003) and extended to labeled dependency parsing by Nivre et al. (2004).
Citation Sentence:
The parsing algorithm used for all languages is the deterministic algorithm first proposed for unlabeled dependency parsing by Nivre ( 2003 ) and extended to labeled dependency parsing by Nivre et al. ( 2004 ) .
Context after the citation:
The algorithm builds a labeled dependency graph in one left-to-right pass over the input, using a stack to store partially processed tokens and adding arcs using four elementary actions (where top is the token on top of the stack and next is the next token): ⢠SHIFT: Push next onto the stack. ⢠REDUCE: Pop the stack. ⢠RIGHT-ARC(r): Add an arc labeled r from top to next; push next onto the stack.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:365
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
The semantic categories of verbs and other words are extracted from the Semantic Knowledge-base of Contemporary Chinese (Wang et al. 2003). SemCat (semantic category) of predicate, SemCat of first word, SemCat of head word, SemCat of last word, SemCat of predicate + SemCat of first word, SemCat of predicate + SemCat of last word, predicate + SemCat of head word, SemCat of predicate + head word. layer of the constituent in focus, the number of constituents in the ascending part of the path subtracted by the number of those in the descending part of path, e.g. if the path is PP-BNFâVPâVP âVV, the feature extracted by this template will be -1.
Citation Sentence:
The semantic categories of verbs and other words are extracted from the Semantic Knowledge-base of Contemporary Chinese ( Wang et al. 2003 ) .
Context after the citation:
verb AllFrameSets, the combination of all the framesets of a predicate. verb class + verb AllFrameSets, verb AllFrameSets + head word, verb AllFrameSets + phrase type. There are more than 40 feature templates, and it is quite difficult to traverse all the possible combinations and get the best one. So we use a greedy algorithm to get an approximate optimal solution.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:366
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Moreover, in order to determine whether the performances of the predictive criteria are consistent across different learning models within the same domain, we have performed the study on two parsing models: one based on a context-free variant of tree-adjoining grammars (Joshi, Levy, and Takahashi 1975), the Probabilistic Lexicalized Tree Insertion Grammar (PLTIG) formalism (Schabes and Waters 1993; Hwa 1998), and Collinsâs Model 2 parser (1997). In this section, we investigate whether these observations hold true for training statistical parsing models as well. Although knowledge about the problem space seems to help sharpening the learning curve initially, overall, it is not a good predictor.
Citation Sentence:
Moreover , in order to determine whether the performances of the predictive criteria are consistent across different learning models within the same domain , we have performed the study on two parsing models : one based on a context-free variant of tree-adjoining grammars ( Joshi , Levy , and Takahashi 1975 ) , the Probabilistic Lexicalized Tree Insertion Grammar ( PLTIG ) formalism ( Schabes and Waters 1993 ; Hwa 1998 ) , and Collins 's Model 2 parser ( 1997 ) .
Context after the citation:
Although both models are lexicalized, statistical parsers, their learning algorithms are different. The Collins Parser is a fully supervised, history-based learner that models the parameters of the parser by taking statistics directly from the training data. In contrast, PLTIGâs expectation-maximization-based induction algorithm is partially supervised; the modelâs parameters are estimated indirectly from the training data. As a superset of the PP-attachment task, parsing is a more challenging learning problem.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:367
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
6 The Partial-VP Topicalization Lexical Rule proposed by Hinrichs and Nakazawa (1994, 10) is a linguistic example. 5 An in-depth discussion including a comparison of both approaches is provided in Calcagno, Meurers, and Pollard (in preparation). 4 This interpretation of the signature is sometimes referred to as closed world (Gerdemann and King 1994; Gerdemann 1995).
Citation Sentence:
6 The Partial-VP Topicalization Lexical Rule proposed by Hinrichs and Nakazawa ( 1994 , 10 ) is a linguistic example .
Context after the citation:
The in-specification of this lexical rule makes use of an append relation to constrain the valence attribute of the auxiliaries serving as its input. In the lexicon, however, the complements of an auxiliary are uninstantiated because it raises the arguments of its verbal complement. The extended lexicon under the DLR approach. partially be dealt with, for example, by using a depth bound on lexical rule application to ensure that a finite number of lexical entries is obtained.'
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:368
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
method
Context before the citation:
We measure the inter annotator agreement using the Fleiss Kappa (Fleiss et al., 1981) measure (x) where the agreement lies around 0.79. Out of this, 1500 verb sequences are unique to each of them and rest 500 are overlapping. Each linguist has received 2000 verb pairs along with their respective example sentences.
Citation Sentence:
We measure the inter annotator agreement using the Fleiss Kappa ( Fleiss et al. , 1981 ) measure ( x ) where the agreement lies around 0.79 .
Context after the citation:
Next, out of the 500 common verb sequences that were annotated by all the three linguists, we randomly choose 300 V1+V2 pairs and presented them to 36 native Bangla speakers. We ask each subjects to give a compositionality score of each verb sequences under 1-10 point scale, 10 being highly compositional and 1 for noncompositional. We found an agreement of x=0.69 among the subjects. We also observe a continuum of compositionality score among the verb sequences.
|
Uses
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:369
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
All current approaches to monolingual TE, either syntactically oriented (Rus et al., 2005), or applying logical inference (Tatu and Moldovan, 2005), or adopting transformation-based techniques (Kouleykov and Magnini, 2005; Bar-Haim et al., 2008), incorporate different types of lexical knowledge to support textual inference. 2 Lexical resources for TE and CLTE Section 6 concludes the paper, and outlines the directions of our future research.
Citation Sentence:
All current approaches to monolingual TE , either syntactically oriented ( Rus et al. , 2005 ) , or applying logical inference ( Tatu and Moldovan , 2005 ) , or adopting transformation-based techniques ( Kouleykov and Magnini , 2005 ; Bar-Haim et al. , 2008 ) , incorporate different types of lexical knowledge to support textual inference .
Context after the citation:
Such information ranges from i) lexical paraphrases (textual equivalences between terms) to ii) lexical relations preserving entailment between words, and iii) wordlevel similarity/relatedness scores. WordNet, the most widely used resource in TE, provides all the three types of information. Synonymy relations can be used to extract lexical paraphrases indicating that words from the text and the hypothesis entail each other, thus being interchangeable. Hypernymy/hyponymy chains can provide entailmentpreserving relations between concepts, indicating that a word in the hypothesis can be replaced by a word from the text.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:37
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
experiments
Context before the citation:
The task we used to compare different generalisation techniques is similar to that used by Pereira et al. (1993) and Rooth et al. (1999).
Citation Sentence:
The task we used to compare different generalisation techniques is similar to that used by Pereira et al. ( 1993 ) and Rooth et al. ( 1999 ) .
Context after the citation:
The task is to decide which of two verbs, v and vi, is more likely to take a given noun, n, as an object. The test and training data were obtained as follows. A number of verb direct object pairs were extracted from a subset of the BNC, using the system of Briscoe and Carroll. All those pairs containing a noun not in WordNet were removed, and each verb and argument was lemmatised.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:370
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Other milestones of recent research include the deployment of probabilistic and machine learning techniques (Aone and Bennett 1995; Kehler 1997; Ge, Hale, and Charniak 1998; Cardie and Wagstaff 1999; the continuing interest in centering, used either in original or in revised form (Abracos and Lopes 1994; Strube and Hahn 1996; Hahn and Strube 1997; Tetreault 1999); and proposals related to the evaluation methodology in anaphora resolution (Mitkov 1998a, 2001b). Against the background of a growing interest in multilingual NLP, multilingual anaphora /coreference resolution has gained considerable momentum in recent years (Aone and McKee 1993; Azzam, Humphreys, and Gaizauskas 1998; Harabagiu and Maiorano 2000; Mitkov and Barbu 2000; Mitkov 1999; Mitkov and Stys 1997; Mitkov, Belguith, and Stys 1998). The last decade of the 20th century saw a number of anaphora resolution projects for languages other than English such as French, German, Japanese, Spanish, Portuguese, and Turkish.
Citation Sentence:
Other milestones of recent research include the deployment of probabilistic and machine learning techniques ( Aone and Bennett 1995 ; Kehler 1997 ; Ge , Hale , and Charniak 1998 ; Cardie and Wagstaff 1999 ; the continuing interest in centering , used either in original or in revised form ( Abracos and Lopes 1994 ; Strube and Hahn 1996 ; Hahn and Strube 1997 ; Tetreault 1999 ) ; and proposals related to the evaluation methodology in anaphora resolution ( Mitkov 1998a , 2001b ) .
Context after the citation:
For a more detailed survey of the state of the art in anaphora resolution, see Mitkov (forthcoming). The papers published in this issue reflect the major trends in anaphora resolution in recent years. Some of them describe approaches that do not exploit full syntactic knowledge (as in the case of Palomar et al.'s and Stuckardt's work) or that employ machine learning techniques (Soon, Ng, and Lim); others present centering-based pronoun resolution (Tetreault) or discuss theoretical centering issues (Kibble). Almost all of the papers feature extensive evaluation (including comparative evaluation as in the case of Tetreault's and Palomar et al.'s work) or discuss general evaluation issues (Byron as well as Stuckardt).
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:371
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
conclusion
Context before the citation:
It is not aimed at handling dependencies, which require heavy use of lexical information (Hindle and Rooth, 1993, for PP attachment). The presented method concerns primarily with phrases, which can be represented by a tree structure. Analogously, the denominator in MBSL would be Freq(Y).
Citation Sentence:
It is not aimed at handling dependencies , which require heavy use of lexical information ( Hindle and Rooth , 1993 , for PP attachment ) .
Context after the citation:
As (Daelemans et al., 1999) show, lexical information improves on NP and VP chunking as well. Since our method uses raw data, representing lexical entries will require a lot of memory. In a future work, we plan to use the system for providing instance candidates, and disambiguate them using an algorithm more suitable for handling lexical information. An additional possibility is to use word-types, such as a special tag for be-verbs, or for prepositions like 'of' which attaches mainly to nouns (Sekine and Grishman, 1995).
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:372
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
This includes work on question answering (Wang et al., 2007), sentiment analysis (Nakagawa et al., 2010), MT reordering (Xu et al., 2009), and many other tasks. The accuracy and speed of state-of-the-art dependency parsers has motivated a resumed interest in utilizing the output of parsing as an input to many downstream natural language processing tasks.
Citation Sentence:
This includes work on question answering ( Wang et al. , 2007 ) , sentiment analysis ( Nakagawa et al. , 2010 ) , MT reordering ( Xu et al. , 2009 ) , and many other tasks .
Context after the citation:
In most cases, the accuracy of parsers degrades when run on out-of-domain data (Gildea, 2001; McClosky et al., 2006; Blitzer et al., 2006; Petrov et al., 2010). But these accuracies are measured with respect to gold-standard out-of-domain parse trees. There are few tasks that actually depend on the complete parse tree. Furthermore, when evaluated on a downstream task, often the optimal parse output has a model score lower than the best parse as predicted by the parsing model.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:373
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
I A more detailed discussion of various aspects of the proposed parser can be found in (Minnen, 1998). As shown in (Minnen, 1996) â¢The presented research was carried out at the University of Tubingen, Germany, as part of the Sonderforschungsbereich 340. See, among others, (Ramakrishnan et al. 1992).
Citation Sentence:
I A more detailed discussion of various aspects of the proposed parser can be found in ( Minnen , 1998 ) .
Context after the citation:
magic is an interesting technique with respect to natural language processing as it incorporates filtering into the logic underlying the grammar and enables elegant control independent filtering improvements. In this paper we investigate the selective application of magic to typed feature grammars a type of constraint-logic grammar based on Typed Feature Logic (T r; G6tz, 1995). Typed feature grammars can be used as the basis for implementations of Head-driven Phrase Structure Grammar (HPSG; Pollard and Sag, 1994) as discussed in (Gotz and Meurers, 1997a) and (Meurers and Minnen, 1997). Typed feature grammar constraints that are inexpensive to resolve are dealt with using the top-down interpreter of the ConTroll grammar development system (GOtz and Meurers, 1997b) which uses an advanced search function, an advanced selection function and incorporates a coroutining mechanism which supports delayed interpretation.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:374
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
related work
Context before the citation:
Our experimental design with professional bilingual translators follows our previous work Green et al. (2013a) comparing scratch translation to post-edit. However, he used undergraduate, non-professional subjects, and did not consider re-tuning. The process study most similar to ours is that of Koehn (2009a), who compared scratch, post-edit, and simple interactive modes.
Citation Sentence:
Our experimental design with professional bilingual translators follows our previous work Green et al. ( 2013a ) comparing scratch translation to post-edit .
Context after the citation:
Many research translation UIs have been proposed including TransType (Langlais et al., 2000), Caitra (Koehn, 2009b), Thot (Ortiz-MartÃnez and Casacuberta, 2014), TransCenter (Denkowski et al., 2014b), and CasmaCat (Alabau et al., 2013). However, to our knowledge, none of these interfaces were explicitly designed according to mixedinitiative principles from the HCI literature. Incremental MT learning has been investigated several times, usually starting from no data (Barrachina et al., 2009; Ortiz-MartÃnez et al., 2010), via simulated post-editing (MartÃnez-Gómez et al., 2012; Denkowski et al., 2014a), or via re-ranking (Wäschle et al., 2013). No previous experiments combined large-scale baselines, full re-tuning of the model weights, and HTER optimization.
|
Extends
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:375
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Lexical functional grammar (Kaplan and Bresnan 1982; Bresnan 2001; Dalrymple 2001) is a member of the family of constraint-based grammars.
Citation Sentence:
Lexical functional grammar ( Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ) is a member of the family of constraint-based grammars .
Context after the citation:
It posits minimally two levels of syntactic representation:2 c(onstituent)-structure encodes details of surface syntactic constituency, whereas f(unctional)-structure expresses abstract syntactic information about predicateâargumentâmodifier relations and certain morphosyntactic properties such as tense, aspect, and case. C-structure takes the form of phrase structure trees and is defined in terms of CFG rules and lexical entries. F-structure is produced from functional annotations on the nodes of the c-structure and implemented in terms of recursive feature structures (attributeâvalue matrices). This is exemplified by the analysis of the string The inquiry soon focused on the judge (wsj 0267 72) using the grammar in Figure 1, which results in the annotated c-structure and f-structure in Figure 2.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:376
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
None
Context before the citation:
Thus, the second class of SBD systems employs machine learning techniques such as decision tree classifiers (Riley 1989), neural networks (Palmer and Hearst 1994), and maximum-entropy modeling (Reynar and Ratnaparkhi 1997). Automatically trainable software is generally seen as a way of producing systems that are quickly retrainable for a new corpus, for a new domain, or even for another language. Another well-acknowledged shortcoming of rule-based systems is that such systems are usually closely tailored to a particular corpus or sublanguage and are not easily portable across domains.
Citation Sentence:
Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .
Context after the citation:
Machine learning systems treat the SBD task as a classification problem, using features such as word spelling, capitalization, suffix, and word class found in the local context of a potential sentence-terminating punctuation sign. Although training of such systems is completely automatic, the majority of machine learning approaches to the SBD task require labeled examples for training. This implies an investment in the data annotation phase.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:377
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
conclusion
Context before the citation:
Previously, a user study (Lin et al. 2003) has shown that people are reluctant to type full natural language questions, even after being told that they were using a questionanswering system and that typing complete questions would result in better performance. The design and implementation of our current system leaves many open avenues for future exploration, one of which concerns our assumptions about the query interface.
Citation Sentence:
Previously , a user study ( Lin et al. 2003 ) has shown that people are reluctant to type full natural language questions , even after being told that they were using a questionanswering system and that typing complete questions would result in better performance .
Context after the citation:
We have argued that a query interface based on structured PICO frames will yield better-formulated queries, although it is unclear whether physicians would invest the upfront effort necessary to accomplish this. Issuing extremely short queries appears to be an ingrained habit of information seekers today, and the dominance of World Wide Web searches reinforce this behavior. Given these trends, physicians may actually prefer the rapid back-and-forth interaction style that comes with short queries.
|
CompareOrContrast
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:378
|
You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.
Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.
Section Title:
introduction
Context before the citation:
We will examine the worst-case complexity of interpretation as well as generation to shed some light on the hypothesis that vague descriptions are more difficult to process than others because they involve a comparison between objects (Beun and Cremers 1998, Krahmer and Theune 2002).
Citation Sentence:
We will examine the worst-case complexity of interpretation as well as generation to shed some light on the hypothesis that vague descriptions are more difficult to process than others because they involve a comparison between objects ( Beun and Cremers 1998 , Krahmer and Theune 2002 ) .
Context after the citation:
Before we do this, consider the tractability of the original IA. If the running time of FindBestValue(r,Ai) is a constant times the number of Values of the Attribute Ai, then the worst-case running time of IA (and IAPlur) is O(n77na), where na equals the number of Attributes in the language and n77 the average number of Values of all Attributes. This is because, in the worst case, all Values of all Attributes need to be attempted (van Deemter 2002). As for the new algorithm, we focus on the crucial phases 2, 4, and 5.
|
Background
|
{
"domains": [
"artificial_intelligence"
],
"input_context": "multiple_paragraphs",
"output_context": "label",
"source_type": "single_source",
"task_family": "classification"
}
|
acl_arc_intent_classification:train:379
|
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