Integrating graph convolutional networks to enhance prompt learning for biomedical relation extraction.

Journal: Journal of biomedical informatics
Published Date: Aug 28, 2024

Abstract

BACKGROUND AND OBJECTIVE: Biomedical relation extraction aims to reveal the relation between entities in medical texts. Currently, the relation extraction models that have attracted much attention are mainly to fine-tune the pre-trained language models (PLMs) or add template prompt learning, which also limits the ability of the model to deal with grammatical dependencies. Graph convolutional networks (GCNs) can play an important role in processing syntactic dependencies in biomedical texts.

Authors

  • Bocheng Guo
    School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.
  • Jiana Meng
    School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.
  • Di Zhao
  • Xiangxing Jia
    School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.
  • Yonghe Chu
    College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, Henan, China.
  • Hongfei Lin

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