Comparing neural models for nested and overlapping biomedical event detection.

Journal: BMC bioinformatics
Published Date: Jun 2, 2022

Abstract

BACKGROUND: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events.

Authors

  • Kurt Espinosa
    National Centre for Text Mining, Department of Computer Science, The University of Manchester, Manchester, UK.
  • Panagiotis Georgiadis
    National Centre for Text Mining, Department of Computer Science, The University of Manchester, Manchester, UK.
  • Fenia Christopoulou
    National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
  • Meizhi Ju
    College of Biomedical Engineering and Instrument Science, Zhejiang University, The Key Laboratory of Biomedical Engineering, Ministry of Education, Hangzhou, China.
  • Makoto Miwa
  • Sophia Ananiadou

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