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

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

Mourad Sarrouti, Said Ouatik El Alaoui
Copyright: © 2017 |Volume: 12 |Issue: 3 |Pages: 13
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781522511670|DOI: 10.4018/IJHISI.2017070104
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MLA

Sarrouti, Mourad, and Said Ouatik El Alaoui. "A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering." IJHISI vol.12, no.3 2017: pp.62-74. http://doi.org/10.4018/IJHISI.2017070104

APA

Sarrouti, M. & El Alaoui, S. O. (2017). A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering. International Journal of Healthcare Information Systems and Informatics (IJHISI), 12(3), 62-74. http://doi.org/10.4018/IJHISI.2017070104

Chicago

Sarrouti, Mourad, and Said Ouatik El Alaoui. "A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering," International Journal of Healthcare Information Systems and Informatics (IJHISI) 12, no.3: 62-74. http://doi.org/10.4018/IJHISI.2017070104

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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.

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