Neural architectures for fine-grained entity type classification

Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

In this work, we investigate several neural 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 establish 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 parameter sharing between labels through a hierarchical encoding method, that in lowdimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85% loose micro 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.

Original languageEnglish
Title of host publicationLong Papers - Continued
PublisherAssociation for Computational Linguistics (ACL)
Pages1271-1280
Number of pages10
Volume1
ISBN (Electronic)9781510838604
Publication statusPublished - 2017 Jan 1
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 2017 Apr 32017 Apr 7

Other

Other15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
CountrySpain
CityValencia
Period17/4/317/4/7

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

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  • Cite this

    Shimaoka, S., Stenetorp, P., Inui, K., & Riedel, S. (2017). Neural architectures for fine-grained entity type classification. In Long Papers - Continued (Vol. 1, pp. 1271-1280). Association for Computational Linguistics (ACL).