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Automated Contradiction Detection in Biomedical Literature

  • Noha S. TawfikEmail author
  • Marco R. Spruit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Biomedical NLP Answer selection Contradiction detection Information extraction Text mining 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Engineering Department, College of EngineeringArab Academy for Science, Technology, and Maritime Transport (AAST)AlexandriaEgypt
  2. 2.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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