Chinese Named Entity Recognition Fusing Lexical and Syntactic Information

Abstract

Chinese named entity recognition is an important research field in natural language processing and is a significant factor in multitasking. However, the accuracy of Chinese named entity recognition is impaired by the lack of entity boundary information and the neglect of potential syntactic information. In this context. In the present study, a Chinese named entity recognition fusing lexical and syntactic information was proposed, and the steps are as follows. Firstly, input text is mapped to character vectors, and external lexical information is introduced with an improved word set matching method and integrated into the input representation of each character. Secondly, BiLSTM is used to obtain the context vector based on the input representation of the character. Thirdly, the syntactic vector is constructed by means of the key value memory network, and the feature vector is obtained by the weighted fusion of context vector and syntactic vector by the gating mechanism. Finally, the feature vector is input into CRF to realize Chinese named entity recognition. The experimental results reveal that the proposed method had better performance on Resume, Weibo and MSRA, and was superior to the current mainstream Chinese named entity recognition methods.