Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification.

Journal: Journal of biomedical semantics
Published Date: May 11, 2016

Abstract

BACKGROUND: Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have been introduced to solve this task. Past studies have shown that the identification of the phrases describing biological processes, also known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. In this paper we propose a novel approach for filtering falsely identified triggers from large-scale event databases, thus improving the quality of knowledge extraction.

Authors

  • Farrokh Mehryary
    Department of Information Technology, University of Turku, Turku, Finland ; The University of Turku Graduate School (UTUGS), University of Turku, Turku, Finland.
  • Suwisa Kaewphan
    Department of Information Technology, University of Turku, Turku, Finland ; The University of Turku Graduate School (UTUGS), University of Turku, Turku, Finland ; Turku Centre for Computer Science (TUCS), Turku, Finland.
  • Kai Hakala
    Department of Information Technology, University of Turku, Turku, Finland ; The University of Turku Graduate School (UTUGS), University of Turku, Turku, Finland.
  • Filip Ginter
    Department of Information Technology, University of Turku, Turku, Finland.

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