ClassiNet – Predicting Missing Features for Short-Text Classification
Table 3: Comparison against sentence-level embedding methods.
['Method', '[BOLD] MR', '[BOLD] CR', '[BOLD] SUBJ', '[BOLD] TREC']
Performance of these methods on the TR dataset were not available. As a multiclass classification setting, we used the TREC question-type classification dataset. In this dataset, each question is manually classified to 6 question types depending on the information asked in the question such as abbreviation, entity, description, human, location and numeric. We use the same classinet as we used in the binary classification tasks to predict features for 5500 train and 500 test questions. A multiclass logistic regression classifier is trained on feature vectors with missing features predicted and tested on the feature vectors for the test questions with missing features predicted. However, unlike Skip-though which considers the word-order in a sentence, FastSent models a sentence as a bag-of-words. Paragraph2Vec a sentence) in a corpus jointly with word embeddings for every word in that corpus such that the word embeddings are shared across all short-texts in the corpus. Sequential Denoising Autoencoder (SDAE) ( We use the SDAE version that uses pre-trained CBOW embeddings to initialise the word embeddings because of its superior performance over the SDAE version that uses randomly initialised word embeddings. However, unlike Skip-thought that is trained for two weeks on a GPU cluster, ClassiNets can be trained in less than 6 hours end-to-end on a single core CPU. The computational efficiency of ClassiNets is particularly attractive when continuously classifying large amounts of short-texts such as, for example, sentiment classification of tweets coming in as a continuous data stream.