Joint learning-based causal relation extraction from biomedical literature

Dongling Li, Pengchao Wu, Yuehu Dong, Jinghang Gu, Longhua Qian, Guodong Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.

Original languageEnglish
Article number104318
JournalJournal of Biomedical Informatics
Early online date11 Feb 2023
Publication statusPublished - Mar 2023


  • BEL Statement
  • Function Detection
  • Joint Learning
  • Relation Extraction

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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