Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts

Enrique Noriega-Atala, Paul D. Hein, Shraddha S. Thumsi, Zechy Wong, Xia Wang, Sean M. Hendryx, Clayton T. Morrison

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study.

Original languageEnglish (US)
Article number8664185
Pages (from-to)1895-1906
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Issue number6
DOIs
StatePublished - Nov 1 2020

Keywords

  • Context
  • NLP
  • bioinformatics
  • data mining
  • inter-sentence relation extraction

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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