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Recognizing Scientific Artifacts in Biomedical Literature.

Today's search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central th...

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Published in:Biomedical Informatics Insights Vol. 6; pp. 15 - 28
Main Authors: Groza, Tudor, Hassanzadeh, Hamed, Hunter, Jane
Format: research Journal Article
Published: Sage Publications Inc. 2013
Online Access:View this record in EBSCOhost
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      dt: 2013
      vid: 6
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Recognizing Scientific Artifacts in Biomedical Literature.
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          Groza, Tudor
          Hassanzadeh, Hamed
          Hunter, Jane
        affil: School of ITee, University of Queensland, Australia
      sug:
        subj:
          Knowledge
          Information Retrieval Methods
          Medical Literature
          Automation
          Natural Language Processing
          Classification
          Linguistics
          Validation Studies
          Funding Source
      ab: Today's search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. The performance achieved by the classifiers ranges from 15.30% to 78.39%, subject to the target class. The set of features used for classification has led to promising results. Furthermore, their use strictly in a local, publication scope, ie, without aggregating corpus-wide statistics, increases the versatility of the ensemble of classifiers and enables its direct applicability without the necessity of re-training.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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