Type Enhanced BERT for Correcting NER Errors

We introduce the task of correcting named entity recognition (NER) errors without retraining the model. After a 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 the NER model's output. The experiment results in multiple corpus show the effectiveness of our method, which outperforms strong baselines.