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Automated Contradiction Detection in Biomedical Literature

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10934))

Abstract

Medical literature suffers from inconsistencies between reported findings that answer the same research question. This paper introduces an automated two-phase contradiction detection model that integrates semantic properties as input features to a Learning-to-Rank framework, to accurately identify key findings of a research article. It also relies on negation, antonyms and similarity measures to detect contradictions between findings. The proposed technique is implemented and tested on a publicly available contradiction corpus 259 manually annotated abstracts. The performance is compared based on recall, precision and F-measure. Experimental evaluations prove the utility of the model and its contribution to the contradiction classification and extraction task.

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Notes

  1. 1.

    Corpus available at http://staffwww.dcs.shef.ac.uk/people/M.Stevenson/resources/bio_contradictions/.

  2. 2.

    https://github.com/rmit-ir/SummaryRank.

  3. 3.

    https://sourceforge.net/p/lemur/wiki/RankLib/.

  4. 4.

    https://spacy.io/.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, College of Engineering, Arab Academy for Science, Technology, and Maritime Transport (AAST), Abukir, Alexandria, 1029, Egypt

    Noha S. Tawfik

  2. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Noha S. Tawfik & Marco R. Spruit

Authors
  1. Noha S. Tawfik
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  2. Marco R. Spruit
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Correspondence to Noha S. Tawfik .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Tawfik, N.S., Spruit, M.R. (2018). Automated Contradiction Detection in Biomedical Literature. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-96136-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96135-4

  • Online ISBN: 978-3-319-96136-1

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