Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition

Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang

Abstract
Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck. To alleviate this problem, this paper proposes Gazetteer-Enhanced Attentive Neural Networks, which can enhance region-based NER by learning name knowledge of entity mentions from easily-obtainable gazetteers, rather than only from fully-annotated data. Specially, we first propose an attentive neural network (ANN), which explicitly models the mention-context association and therefore is convenient for integrating externally-learned knowledge. Then we design an auxiliary gazetteer network, which can effectively encode name regularity of mentions only using gazetteers. Finally, the learned gazetteer network is incorporated into ANN for better NER. Experiments show that our ANN can achieve the state-of-the-art performance on ACE2005 named entity recognition benchmark. Besides, incorporating gazetteer network can further improve the performance and significantly reduce the requirement of training data.
Anthology ID:
D19-1646
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6232–6237
Language:
URL:
https://aclanthology.org/D19-1646
DOI:
10.18653/v1/D19-1646
Bibkey:
Cite (ACL):
Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, and Shanshan Jiang. 2019. Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6232–6237, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (Lin et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/ingest-2024-clasp/D19-1646.pdf
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