Open AccessBiomedical Named Entity Recognition Based on Self‐supervised Deep Belief Network
Author(s) -
Zhang Yajun,
Liu Zongtian,
Zhou Wen
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.03.001
Subject(s) - deep belief network , computer science , artificial intelligence , construct (python library) , feature (linguistics) , pattern recognition (psychology) , measure (data warehouse) , feature vector , layer (electronics) , machine learning , deep learning , data mining , linguistics , philosophy , chemistry , organic chemistry , programming language
Named entity recognition is a fundamental and crucial issue of biomedical data mining. For effectively solving this issue, we propose a novel approach based on Deep belief network (DBN). We select nine entity features, and construct feature vector mapping tables by the recognition contribution degree of different values of them. Using the mapping tables, we transform words in biomedical texts to feature vectors. The DBN will identify entities by reducing dimensions of vector data. The extensive experimental results reveal that the novel approach has achieved excellent recognition performance, with 69.96% maximum value of F ‐measure on GENIA 3.02 testing corpus. We propose a self‐supervised DBN, which can decide whether to add supervised fine‐tuning or not according to the recognition performance of each layer, can overcome the errors propagation problem, while the complexity of model is limited. Test analysis shows that the new DBN improves recognition performance, the F ‐measure increases to 72.12%.