Abstract
This paper utilizes the BERTbased-BiLSTM-CRF models to complete Chinese named entity recognition tasks, including finetuned and unfinetuned BERT models. Use the pre-training model BERT(Bidirectional Encoder Representations from Transformers), a BiLSTM(Bi-directional Long Short-Term Memory) network and CRF(Conditional Random Field) to perform NER(Named Entity Recognition) on Chinese. Tested on the people-daily-ner-pretreatment corpus, compared with other models, the BERTbased models can effectively identify entity information, and the evaluation metrics on the dataset have been improved.
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