Chinese Named Entity Recognition Algorithm Integrating Vocabulary Information

In the current Chinese NER task, the language model especially has problems such as the accuracy of Chinese boundary entity recognition and the insufficient learning of Chinese character vocabulary information during the training process. This article proposes an entity recognition model LEBERT-IDGRU-CRF based on BERT and introducing external dictionaries for training. The model performs lexical matching on the data text through an external dictionary to construct word pairs, and then passes the vector matrix to the feature extraction layer, which introduces an attention mechanism for further extraction. Through comparative experiments on four data sets, the model results were improved and the feasibility of the model was verified.