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Evidence Type Classification in Randomized Controlled Trials

Tobias Mayer 1 Elena Cabrio 1 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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.
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Contributor : Tobias Mayer <>
Submitted on : Monday, November 5, 2018 - 11:07:38 AM
Last modification on : Thursday, January 21, 2021 - 1:34:13 PM
Long-term archiving on: : Wednesday, February 6, 2019 - 2:11:03 PM


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  • HAL Id : hal-01912157, version 1


Tobias Mayer, Elena Cabrio, Serena Villata. Evidence Type Classification in Randomized Controlled Trials. 5th ArgMining@EMNLP 2018, Oct 2018, Brussels, Belgium. ⟨hal-01912157⟩



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