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://aclanthology.org/W18-5204
DOI:
10.18653/v1/W18-5204
Bibkey:
Cite (ACL):
Tobias Mayer, Elena Cabrio, and Serena Villata. 2018. Evidence Type Classification in Randomized Controlled Trials. In Proceedings of the 5th Workshop on Argument Mining, pages 29–34, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Evidence Type Classification in Randomized Controlled Trials (Mayer et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5204.pdf