<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mayer-etal-2018-evidence">
    <titleInfo>
        <title>Evidence Type Classification in Randomized Controlled Trials</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Tobias</namePart>
        <namePart type="family">Mayer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Cabrio</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Serena</namePart>
        <namePart type="family">Villata</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on Argument Mining</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Noam</namePart>
            <namePart type="family">Slonim</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ranit</namePart>
            <namePart type="family">Aharonov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">mayer-etal-2018-evidence</identifier>
    <identifier type="doi">10.18653/v1/W18-5204</identifier>
    <location>
        <url>https://aclanthology.org/W18-5204</url>
    </location>
    <part>
        <date>2018-11</date>
        <extent unit="page">
            <start>29</start>
            <end>34</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Evidence Type Classification in Randomized Controlled Trials
%A Mayer, Tobias
%A Cabrio, Elena
%A Villata, Serena
%Y Slonim, Noam
%Y Aharonov, Ranit
%S Proceedings of the 5th Workshop on Argument Mining
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mayer-etal-2018-evidence
%X 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.
%R 10.18653/v1/W18-5204
%U https://aclanthology.org/W18-5204
%U https://doi.org/10.18653/v1/W18-5204
%P 29-34

Markdown (Informal)

[Evidence Type Classification in Randomized Controlled Trials](https://aclanthology.org/W18-5204) (Mayer et al., ArgMining 2018)

• Evidence Type Classification in Randomized Controlled Trials (Mayer et al., ArgMining 2018)

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.