Biomedical named entity normalization via interaction-based synonym marginalization.

Journal: Journal of biomedical informatics
Published Date: Nov 15, 2022

Abstract

OBJECTIVE: Biomedical named entity normalization (BNEN) is a fundamental natural language processing (NLP) task in the biomedical domain. Many representation learning-based methods have been successfully applied to BNEN in recent years. Most of them encode a given biomedical named entity mention (BNEM) and candidates separately, some of them consider relations between the BNEM and its candidates, however, few consider relations among the candidates, which may be useful for BNEN.

Authors

  • Hao Peng
    Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong, P. R. China.
  • Ying Xiong
    Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.
  • Buzhou Tang

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