@InProceedings{mayer-cabrio-villata:2018:W18-52,
  author    = {Mayer, Tobias  and  Cabrio, Elena  and  Villata, Serena},
  title     = {Evidence Type Classification in Randomized Controlled Trials},
  booktitle = {Proceedings of the 5th Workshop on Argument Mining},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {29--34},
  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 \textit{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 \textit{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: \textit{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.},
  url       = {http://www.aclweb.org/anthology/W18-5204}
}