Chinese Nested Named Entity Recognition Algorithm Based on Knowledge Graph.

Nested named entity recognition has been widely used in natural language processing, information extraction and other fields. However, the multiple boundaries of nested named entities make the recognition of single entity face great challenges. This article used the information in the knowledge graph to extract and annotate entities in the Chinese nested named entity recognition algorithm, and built a new named entity recognition model based on this. Experimental results showed that this article applied the knowledge graph to the Chinese nested named entity recognition task, and its accuracy could reach up to 95.3%, which has obvious advantages in complex structure processing, relationship extraction and entity links. [ABSTRACT FROM AUTHOR]