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A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering

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  • Mourad Sarrouti

    (Laboratory of Computer Science and Modeling, FSDM, Sidi Mohammed Ben Abdellah University, Fez, Morocco)

  • Said Ouatik El Alaoui

    (Laboratory of Computer Science and Modeling, FSDM, Sidi Mohammed Ben Abdellah University, Fez, Morocco)

Abstract

Background and Objective: Yes/no question answering (QA) in open-domain is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in the biomedical domain. Yes/no QA aims at answering yes/no questions, which are seeking for a clear “yes” or “no” answer. In this paper, we present a novel yes/no answer generator based on sentiment-word scores in biomedical QA. Methods: In the proposed method, we first use the Stanford CoreNLP for tokenization and part-of-speech tagging all relevant passages to a given yes/no question. We then assign a sentiment score based on SentiWordNet to each word of the passages. Finally, the decision on either the answers “yes” or “no” is based on the obtained sentiment-passages score: “yes” for a positive final sentiment-passages score and “no” for a negative one. Results: Experimental evaluations performed on BioASQ collections show that the proposed method is more effective as compared with the current state-of-the-art method, and significantly outperforms it by an average of 15.68% in terms of accuracy.

Suggested Citation

  • Mourad Sarrouti & Said Ouatik El Alaoui, 2017. "A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(3), pages 62-74, July.
  • Handle: RePEc:igg:jhisi0:v:12:y:2017:i:3:p:62-74
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