Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network

Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu


Abstract
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.
Anthology ID:
D19-1396
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:
3830–3840
Language:
URL:
https://aclanthology.org/D19-1396
DOI:
10.18653/v1/D19-1396
Bibkey:
Cite (ACL):
Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. 2019. Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network. 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 3830–3840, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network (Sui et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1396.pdf
Code
 DianboWork/Graph4CNER
Data
Weibo NER