A neural classification method for supporting the creation of BioVerbNet.

Journal: Journal of biomedical semantics
Published Date: Jan 18, 2019

Abstract

BACKGROUND: VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data.

Authors

  • Billy Chiu
    Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.
  • Olga Majewska
    Language Technology Laboratory, MML, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.
  • Sampo Pyysalo
  • Laura Wey
    Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK.
  • Ulla Stenius
    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 210-171-77, Sweden.
  • Anna Korhonen
    Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK. alk23@cam.ac.uk.
  • Martha Palmer
    Department of Linguistics, University of Colorado at Boulder, Colorado, 80309-0295, USA.

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