BSEN: A BiSiamese Entity Normalization Method for Biomedicine. (December 2020)

• Record Type: Journal Article
• Authors: Peng, Zirong; Yu, Qinyong; Yang, Hui; Wang, Yongli
• Is Part Of: Journal of physics. Volume 1693(2020)
• Publication Date: 2020-12
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1693/1/012087
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Normalization of named entities in the field of biomedicine is an important task in biomedical text data mining. Compared with other tasks in biomedical text mining research, there are relatively few researches on entities normalization. In this article, a BiSiamese entity normalization method for biomedicine (BSEN) is proposed. Firstly, the text similarity algorithm is analyzed, and an improved similarity measurement algorithm for biomedical inverse text frequency and cosine (BIC) is proposed. Secondly, the data set is trained in pairs using BiSiamese network and combined with BIC to calculate text similarity. The entity corresponding to the maximum similarity calculated in the normalization knowledge base is the normalized result obtained by the BSEN method. The verification experiments on the verification data set show that the BSEN has achieved better normalization results than the existing methods.