Chinese Named Entity Recognition based on BERT-CRF Model

Named entity recognition (NER) is an important research direction in natural language processing (NLP). Traditional machine learning algorithms in NER have problems such as low accuracy, highly dependent feature design, poor domain adaptability, and inability to handle the different contexts of multiple meanings of the term in recognizing Chinese entities. Based on these problems, this paper adopts a method based on the BERT-CRF model in Chinese NER. The BERT preprocessing language model generates word vectors that represent contextual semantic information, automatically extract numerous word-level features and semantic features in text, and decodes through the CRF layer generates entity tag sequences. In this paper, the BERT model has been fine-tuned to make the model perform better on NER tasks, and the experimental verification is carried out on the People’s Daily dataset, and the F1 value reaches 94.5%.