Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition

Chun Chen, Fang Kong


Abstract
In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.
Anthology ID:
2021.acl-short.4
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–25
Language:
URL:
https://aclanthology.org/2021.acl-short.4
DOI:
10.18653/v1/2021.acl-short.4
Bibkey:
Cite (ACL):
Chun Chen and Fang Kong. 2021. Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 20–25, Online. Association for Computational Linguistics.
Cite (Informal):
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (Chen & Kong, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.4.pdf
Video:
 https://aclanthology.org/2021.acl-short.4.mp4
Code
 cchen-reese/Boundary-Enhanced-NER
Data
OntoNotes 5.0