Position-aware Attention and Supervised Data Improve Slot Filling

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


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%.
Anthology ID:
D17-1004
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–45
URL:
https://www.aclweb.org/anthology/D17-1004
DOI:
10.18653/v1/D17-1004
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PDF:
https://www.aclweb.org/anthology/D17-1004.pdf
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Video:
 https://vimeo.com/238230211