Abstract : Randomized Controlled Trials (RCT) are a common type of experimental studies in the medical domain for evidence-based decision making. The ability to automatically extract the arguments proposed therein can be of valuable support for clinicians and practitioners in their daily evidence-based decision making activities. Given the peculiarity of the medical domain and the required level of detail, standard approaches to argument component detection in argument(ation) mining are not fine-grained enough to support such activities. In this paper, we introduce a new sub-task of the argument component identification task: evidence type classification. To address it, we propose a supervised approach and we test it on a set of RCT abstracts on different medical topics.
Type de document :
Communication dans un congrès
5th ArgMining@EMNLP 2018, Oct 2018, Brussels, Belgium. Proceedings of the 5th Workshop on Argument Mining, ArgMining@EMNLP 2018
https://hal.archives-ouvertes.fr/hal-01912157
Contributeur : Tobias Mayer
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Soumis le : lundi 5 novembre 2018 - 11:07:38
Dernière modification le : jeudi 21 février 2019 - 11:22:45
Document(s) archivé(s) le : mercredi 6 février 2019 - 14:11:03
Tobias Mayer, Elena Cabrio, Serena Villata. Evidence Type Classification in Randomized Controlled Trials. 5th ArgMining@EMNLP 2018, Oct 2018, Brussels, Belgium. Proceedings of the 5th Workshop on Argument Mining, ArgMining@EMNLP 2018. 〈hal-01912157〉
