@inproceedings{li-etal-2023-type,
title = "Type Enhanced {BERT} for Correcting {NER} Errors",
author = "Li, Kuai and
Chen, Chen and
Yang, Tao and
Du, Tianming and
Yu, Peijie and
Du, Dong and
Zhang, Feng",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.445",
doi = "10.18653/v1/2023.findings-acl.445",
pages = "7124--7131",
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",
}
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<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</abstract>
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%0 Conference Proceedings
%T Type Enhanced BERT for Correcting NER Errors
%A Li, Kuai
%A Chen, Chen
%A Yang, Tao
%A Du, Tianming
%A Yu, Peijie
%A Du, Dong
%A Zhang, Feng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-type
%X 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
%R 10.18653/v1/2023.findings-acl.445
%U https://aclanthology.org/2023.findings-acl.445
%U https://doi.org/10.18653/v1/2023.findings-acl.445
%P 7124-7131
Markdown (Informal)
[Type Enhanced BERT for Correcting NER Errors](https://aclanthology.org/2023.findings-acl.445) (Li et al., Findings 2023)
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.