Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined RepresentationsM.A. ALi and W. Wang are the co-corresponding authors.
Table 1: FGET-RR performance comparison against baseline models
['[EMPTY]', 'Wiki strict', 'Wiki mac-F1', 'Wiki mic-F1', 'OntoNotes strict', 'OntoNotes mac-F1', 'OntoNotes mic-F1', 'BBN strict', 'BBN mac-F1', 'BBN mic-F1']
In this paper, we aim to build a multi-label, multi-class entity typing system that can use distantly supervised data to classify an entity mention into a set of fine-grained entity types based on the context. Specifically, we refine the representations learnt on the noisy data prior to entity typing. Dtrain acquired via distant supervision and manually labeled test data Dtest. Formally, the data set D is a set of sentences/paragraphs for which the entity mentions {mi}Ni=1 (tokens corresponding to the entities), the context {ci}Ni=1 and the candidate type labels {yi}Ni=1∈{0,1}Y (Y-dimensional binary vector with yi,t=1 if tth type corresponds to the true label and zero otherwise) have been pre-identified. Here, the type labels correspond to type hierarchy in the knowledge base ψ with the schema Yψ. We represent the data as a set of triples D={(mi,ci,yi)}Ni=1. We boldface the overall best scores with the previous state-of-the-art underlined. These results show that FGET-RR outperforms all the previous state-of-the-art research by a large margin. Especially noteworthy is the performance of our model on the BBN data outclassing the existing models by a margin of 10.4%, 10.2% and 8.3% in strict accuracy, macro-f1, and micro-f1 respectively. For OntoNotes, our model yields 5.1% improvement in micro-f1 compared to the previous best by \citeauthorDBLP:conf/emnlp/ XinZH0S18 \shortciteDBLP :conf/emnlp/XinZH0S18. For Wiki data, the FGET-RR improves the performance by 7.5%, 4.3% and 4.0% in strict accuracy, macro-f1, and micro-f1 respectively. Such promising results show that refining the mention representations via corpus-level contextual clues help in alleviating the label noise associated with the distantly supervised data.