Domain transformation on biological event extraction by learning methods

Wen Juan Hou*, Bamfa Ceesay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Event extraction and annotation has become a significant focus of recent efforts in biological text mining and information extraction (IE). However, event extraction, event annotation methods, and resources have so far focused almost exclusively on a single domain. State-of-the-art studies on biological event extraction and annotation are typically domain-dependent and domain-restricted. In this paper, we adopt an approach aimed at extracting events and relations for two different tasks by generating a common dataset using transfer learning and structural correspondence learning (SCL). A deep learning event extraction system was developed to evaluate our results. Our approach comprises two stages: (1) generating a dataset from two independent event extraction tasks or domains, and (2) using a classifier model to learn feature patterns from the generated dataset for event and relation extraction. The classifier in the proposed model can extract events and relations irrespective of the domain of the test input. Our study shows that this approach performs competitively compared to domain specific or dependent tasks.

Original languageEnglish
Article number103236
JournalJournal of Biomedical Informatics
Publication statusPublished - 2019 Jul


  • Biological event
  • Event extraction
  • GRN
  • GRNA
  • Multi-domain
  • Neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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