@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",
}

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
Li, Kuai Chen, Chen Yang, Tao Du, Tianming Yu, Peijie Du, Dong Zhang, Feng
Type Enhanced BERT for Correcting NER Errors