Chinese Named Entity Recognition Based on Lexicon Fusion and Dependency Relation
Named entity recognition is an important foundational task in the field of natural language processing,providing valuable data support for many downstream tasks,such as relation extraction and knowledge graph construction.To address the difficulties of word segmentation errors,ambiguous entity boundaries,and contextual dependencies in Chinese named entity recognition,as well as the inability of existing methods to fully utilize lexical information and effectively extract internal text features,this paper proposes a Chinese named entity recognition method based on lexicon fusion and dependency relation.First,the self-matching words of each character in the input text are obtained to generate lexical feature vectors,and word boundary information is obtained according to the position of the character in its self-matching words.The character and lexical feature vectors are fused using biaffine attention mechanism,and the lexical and word boundary information are integrated into the encoding process of the model so that the model can achieve good entity recognition ability.Subsequently,based on dependency syntax,a dependency graph structure of the input text is established,and a Graph Attention Network(GAT)is used to capture the internal dependency features of the input text,enhance the semantic dependency information within the text,and facilitate the differentiation of entity boundaries.Finally,text labels are calculated using a Conditional Random Field(CRF).The proposed method obtains Fl values of 92.10％,80.76％,and 95.66％on the CCKS2017,OntoNote4.0 and MSRA datasets,respectively,which are better than those of the comparison models.
attention mechanismdependency relationlexicon fusionGraph Attention Network(GAT)Chinese named entity recognition
国家科技期刊平台
NSTL
万方数据
