Evidence Type Classification in Randomized Controlled Trials

Tobias Mayer, Elena Cabrio, Serena Villata


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.
Anthology ID:
W18-5204
Volume:
Proceedings of the 5th Workshop on Argument Mining
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
ArgMining | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
29–34
Language:
URL:
https://www.aclweb.org/anthology/W18-5204
DOI:
10.18653/v1/W18-5204
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5204.pdf