%0 Conference Proceedings %T Evidence Type Classification in Randomized Controlled Trials %A Mayer, Tobias %A Cabrio, Elena %A Villata, Serena %Y Slonim, Noam %Y Aharonov, Ranit %S Proceedings of the 5th Workshop on Argument Mining %D 2018 %8 November %I Association for Computational Linguistics %C Brussels, Belgium %F mayer-etal-2018-evidence %X 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. %R 10.18653/v1/W18-5204 %U https://aclanthology.org/W18-5204 %U https://doi.org/10.18653/v1/W18-5204 %P 29-34