Leveraging Multi-source knowledge for Chinese clinical named entity recognition via relational graph convolutional network.

Journal: Journal of biomedical informatics
Published Date: Feb 23, 2022

Abstract

OBJECTIVE: External knowledge, such as lexicon of words in Chinese and domain knowledge graph (KG) of concepts, has been recently adopted to improve the performance of machine learning methods for named entity recognition (NER) as it can provide additional information beyond context. However, most existing studies only consider knowledge from one source (i.e., either lexicon or knowledge graph) in different ways and consider lexicon words or KG concepts independently with their boundaries. In this paper, we focus on leveraging multi-source knowledge in a unified manner where lexicon words or KG concepts are well combined with their boundaries for Chinese Clinical NER (CNER).

Authors

  • Ying Xiong
    Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.
  • 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.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Ka-Chun Wong
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
  • Buzhou Tang

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