MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang


Abstract
NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary(OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rotate memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.
Anthology ID:
2022.acl-long.383
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5590–5600
Language:
URL:
https://aclanthology.org/2022.acl-long.383
DOI:
10.18653/v1/2022.acl-long.383
Bibkey:
Cite (ACL):
Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, and Xuanjing Huang. 2022. MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5590–5600, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (Wang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.383.pdf
Code
 beyonderxx/miner
Data
WNUT 2017