Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Nov 25, 2021

Abstract

OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning.

Authors

  • Tiantian Zhu
    Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
  • Yang Qin
    Department of Biochemistry and Molecular Biology, West China School of Preclinical and Forensic Medicine, Sichuan University,Chengdu 610041,China.
  • Yang Xiang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Baotian Hu
    Harbin Institute of Technology (Shenzhen), Shenzhen, China. hubaotian@hit.edu.cn.
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Weihua Peng
    Baidu, International Technology (Shenzhen) Co., Ltd, Shenzhen, China.

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