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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
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Author(s): Mourad Sarrouti (Sidi Mohammed Ben Abdellah University, Morocco) and Said Ouatik El Alaoui (Sidi Mohammed Ben Abdellah University, Morocco)
Copyright: 2020
Pages: 14
Source title: Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch005

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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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