Neural Architectures for Fine-grained Entity Type Classification

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1271–1280, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Neural Architectures for Fine-grained Entity Type Classification Sonse Shimaoka†∗, Pontus Stenetorp‡, Kentaro Inui† and Sebastian Riedel‡ †Graduate School of Information Sciences, Tohoku University ‡Department of Computer Science, University College London {simaokasonse,inui}@ecei.tohoku.ac.jp {p.stenetorp,s.riedel}@cs.ucl.ac.uk Abstract In this work, we investigate several neu- ral network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and es- tablish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce param- eter sharing between labels through a hi- erarchical encoding method, that in low- dimensional projections show clear clus- ters for each type hierarchy. Lastly, de- spite using the same evaluation dataset, the literature frequently compare models trained using different data. We demon- strate that the choice of training data has a drastic impact on performance, which de- creases by as much as 9.85% loose mi- cro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well-established FIGER (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93% loose micro F1 score. a match series against New Zealand is held on Monday Output Word Embeddings LSTM Layers A9en:ons Context Representa:on Men:on Representa:on /organiza)on, /organiza)on/sports_team Figure 1: An illustration of the attentive en- coder neural model predicting fine-grained seman- tic types for the mention “New Zealand” in the ex- pression “a match series against New Zealand is held on Monday”. 1 Introduction Entity type classification aims to label entity men- tions in their context with their respective semantic types. Information regarding entity type mentions has proven to be valuable for several natural lan- guage processing tasks; such as question answer- ing (Lee et al., 2006), knowledge base popula- tion (Carlson et al., 2010), and co-reference reso- lution (Recasens et al., 2013). A natural extension to traditional entity type classification has been to divide the set of types – which may be too coarse- grained for some applications (Sekine, 2008) – into a larger set of fine-grained entity types (Lee et al., 2006; Ling and Weld, 2012; Yosef et al., 2012; Gillick et al., 2014; Del Corro et al., 2015); for example person into actor, artist, etc. Given the recent successes of attentive neural ∗This work was conducted during a research visit to Uni- versity College London. 1271 models for information extraction (Globerson et al., 2016; Shimaoka et al., 2016; Yang et al., 2016), we investigate several variants of an atten- tive …

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