Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding

Huasha Zhao, Yi Yang, Qiong Zhang, Luo Si

Abstract
Entity recognition is a widely benchmarked task in natural language processing due to its massive applications. The state-of-the-art solution applies a neural architecture named BiLSTM-CRF to model the language sequences. In this paper, we propose an entity recognition system that improves this neural architecture with two novel techniques. The first technique is Multi-Task Data Selection, which ensures the consistency of data distribution and labeling guidelines between source and target datasets. The other one is constrained decoding using knowledge base. The decoder of the model operates at the document level, and leverages global and external information sources to further improve performance. Extensive experiments have been conducted to show the advantages of each technique. Our system achieves state-of-the-art results on the English entity recognition task in KBP 2017 official evaluation, and it also yields very strong results in other languages.
Anthology ID:
N18-2056
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
346–351
Language:
URL:
https://aclanthology.org/N18-2056
DOI:
10.18653/v1/N18-2056
Bibkey:
Cite (ACL):
Huasha Zhao, Yi Yang, Qiong Zhang, and Luo Si. 2018. Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 346–351, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding (Zhao et al., NAACL 2018)
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https://preview.aclanthology.org/nschneid-patch-2/N18-2056.pdf
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