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.
https://hal.archives-ouvertes.fr/hal-01912157 Contributor : Tobias MayerConnect in order to contact the contributor Submitted on : Monday, November 5, 2018 - 11:07:38 AM Last modification on : Friday, January 21, 2022 - 3:10:27 AM Long-term archiving on: : Wednesday, February 6, 2019 - 2:11:03 PM