Entity Enhanced BERT Pre-training for Chinese NER

Chen Jia, Yuefeng Shi, Qinrong Yang, Yue Zhang


Abstract
Character-level BERT pre-trained in Chinese suffers a limitation of lacking lexicon information, which shows effectiveness for Chinese NER. To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semi-supervised entity enhanced BERT pre-training method. In particular, we first extract an entity lexicon from the relevant raw text using a new-word discovery method. We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the self-attention using a combination of character and entity representations. In addition, an entity classification task helps inject the entity information into model parameters in pre-training. The pre-trained models are used for NER fine-tuning. Experiments on a news dataset and two datasets annotated by ourselves for NER in long-text show that our method is highly effective and achieves the best results.
Anthology ID:
2020.emnlp-main.518
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6384–6396
Language:
URL:
https://aclanthology.org/2020.emnlp-main.518
DOI:
10.18653/v1/2020.emnlp-main.518
Bibkey:
Cite (ACL):
Chen Jia, Yuefeng Shi, Qinrong Yang, and Yue Zhang. 2020. Entity Enhanced BERT Pre-training for Chinese NER. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6384–6396, Online. Association for Computational Linguistics.
Cite (Informal):
Entity Enhanced BERT Pre-training for Chinese NER (Jia et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.518.pdf
Video:
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