@inproceedings{mayer-etal-2018-evidence,
    title = "Evidence Type Classification in Randomized Controlled Trials",
    author = "Mayer, Tobias  and
      Cabrio, Elena  and
      Villata, Serena",
    editor = "Slonim, Noam  and
      Aharonov, Ranit",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-5204/",
    doi = "10.18653/v1/W18-5204",
    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."
}