A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.

Journal: Sensors (Basel, Switzerland)
Published Date: May 16, 2023

Abstract

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.

Authors

  • Binbin Shi
    Department of Radiology, Subei People's Hospital of Jiangsu Province, Yangzhou Jiangsu, China.
  • Rongli Fan
    School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
  • Lijuan Zhang
    School of Computer Science and Engineering, Changchun University of Technology, Jilin 130012, China.
  • Jie Huang
    Department of Critical Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Neal Xiong
    Department of Computer Science, Mathematics Sul Ross State University, Alpine, TX 79830, USA.
  • Athanasios Vasilakos
    Center for AI Research, University of Agder, 4879 Grimstad, Norway.
  • Jian Wan
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.

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