Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement

• Published in: IEICE Transactions on Information and Systems Vol. E106.D; no. 5; pp. 1010 - 1017
• Main Authors: WANG, Tianbin, HUANG, Ruiyang, HU, Nan, WANG, Huansha, CHU, Guanghan
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.05.2023; Japan Science and Technology Agency
• Subjects: attention mechanism; Chinese named entity recognition; Dictionaries; Information retrieval; Knowledge representation; Natural language processing; Recognition; semantic knowledge enhancement; Semantics; Vector spaces
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2022EDP7168

Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.