Position-aware attention and supervised data improve slot filling

Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, Christopher D. Manning

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

306 Scopus citations

Abstract

Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset, obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2% to 26.7%.

Original languageEnglish (US)
Title of host publicationEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages35-45
Number of pages11
ISBN (Electronic)9781945626838
DOIs
StatePublished - 2017
Externally publishedYes
Event2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017 - Copenhagen, Denmark
Duration: Sep 9 2017Sep 11 2017

Publication series

NameEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Country/TerritoryDenmark
CityCopenhagen
Period9/9/179/11/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

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