Abstract: In the Chinese environment of open entity types and complex entity structure,the Chinese named entity recognition(CNE) task encounter obvious issues,such as entity boundary judgment errors and low accuracy of entity classification.In order to solve above issues,a Chinese named entity recognition model called MacBERT-SDI-ML has been proposed,which based on the MacBERT pre-training model using characters as encoding units.Firstly,in order to extract richer Chinese semantic features and improve the accuracy of entity recognition,the model adopts MacBERT(the whole word masking for chinese BERT) as the embedding layer.Secondly,in order to further enhance entity representation characteristics and improve the accuracy of entity classification,the model utilizes a dependency syntactic information parser(SDIP) to efficiently extract more abundant dependency information of entities and integrate it into character representation.Additionally,considering the potential variation in character positions across different words,the model incorporates a multi-view lexical information fusion component(MLIF) based on self-attention mechanism to further enhance the boundary features of character representation and improve the accuracy of boundary judgment.Finally,experiment is conducted on the Weibo,OntoNotes and resume datasets,and the results show that the F1 value of the proposed model reaches 72.97%,86.56% and 98.45%,respectively.

Key words: CNER, MacBERT, Lexical information, Dependency information, Pre-training model, Self-attention mechanism