Type Enhanced BERT for Correcting NER Errors
Kuai Li, Chen Chen, Tao Yang, Tianming Du, Peijie Yu, Dong Du, Feng Zhang
Abstract
We introduce the task of correcting named entity recognition (NER) errors without re-training model. After an NER model is trained and deployed in production,it makes prediction errors, which usually need to be fixed quickly. To address this problem, we firstly construct a gazetteer containing named entities and corresponding possible entity types. And then, we propose type enhanced BERT (TyBERT),a method that integrates the named entity’s type information into BERT by an adapter layer. When errors are identified, we can repair the model by updating the gazetteer. In other words, the gazetteer becomes a trigger to control NER model’s output. The experiment results in multiple corpus show the effectiveness of our method, which outperforms strong baselines.x- Anthology ID:
- 2023.findings-acl.445
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7124–7131
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.445
- DOI:
- 10.18653/v1/2023.findings-acl.445
- Cite (ACL):
- Kuai Li, Chen Chen, Tao Yang, Tianming Du, Peijie Yu, Dong Du, and Feng Zhang. 2023. Type Enhanced BERT for Correcting NER Errors. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7124–7131, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Type Enhanced BERT for Correcting NER Errors (Li et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.findings-acl.445.pdf