Abstract
In this paper we present an implemented method for analysing arguments from drug reviews given by patients in medical forums on the web. For this we provide a number of classification rules which allow for the extraction of specific arguments from the drug reviews. For each review we use the extracted arguments to instantiate a Dung argument graph. We undertake an evaluation of the resulting argument graphs by applying Dung’s grounded semantics. We demonstrate a correlation between the arguments in the grounded extension of the graph and the rating provided by the user for that particular drug.
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Acknowledgements
The first author is grateful to the The Royal Free Charity and the EPSRC for funding his PhD studentship. The authors are grateful to the reviewers for their helpful feedback.
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Noor, K., Hunter, A., Mayer, A. (2017). Analysis of Medical Arguments from Patient Experiences Expressed on the Social Web. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_31
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DOI: https://doi.org/10.1007/978-3-319-60045-1_31
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