@incollection{noor_analysis_2017,
	address = {Cham},
	title = {Analysis of {Medical} {Arguments} from {Patient} {Experiences} {Expressed} on the {Social} {Web}},
	volume = {10351},
	isbn = {978-3-319-60044-4 978-3-319-60045-1},
	url = {http://link.springer.com/10.1007/978-3-319-60045-1_31},
	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.},
	language = {en},
	urldate = {2019-05-02},
	booktitle = {Advances in {Artificial} {Intelligence}: {From} {Theory} to {Practice}},
	publisher = {Springer International Publishing},
	author = {Noor, Kawsar and Hunter, Anthony and Mayer, Astrid},
	editor = {Benferhat, Salem and Tabia, Karim and Ali, Moonis},
	year = {2017},
	doi = {10.1007/978-3-319-60045-1_31},
	pages = {285--294},
	file = {Noor et al. - 2017 - Analysis of Medical Arguments from Patient Experie.pdf:/Users/neil.hawkins/Zotero/storage/TWBJN5DU/Noor et al. - 2017 - Analysis of Medical Arguments from Patient Experie.pdf:application/pdf},
}

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