<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="leeuwenberg-moens-2017-structured">
    <titleInfo>
        <title>Structured Learning for Temporal Relation Extraction from Clinical Records</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Artuur</namePart>
        <namePart type="family">Leeuwenberg</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marie-Francine</namePart>
        <namePart type="family">Moens</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Phil</namePart>
            <namePart type="family">Blunsom</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexander</namePart>
            <namePart type="family">Koller</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Valencia, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.</abstract>
    <identifier type="citekey">leeuwenberg-moens-2017-structured</identifier>
    <location>
        <url>https://aclanthology.org/E17-1108</url>
    </location>
    <part>
        <date>2017-04</date>
        <extent unit="page">
            <start>1150</start>
            <end>1158</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Structured Learning for Temporal Relation Extraction from Clinical Records
%A Leeuwenberg, Artuur
%A Moens, Marie-Francine
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F leeuwenberg-moens-2017-structured
%X We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.
%U https://aclanthology.org/E17-1108
%P 1150-1158

Markdown (Informal)

[Structured Learning for Temporal Relation Extraction from Clinical Records](https://aclanthology.org/E17-1108) (Leeuwenberg & Moens, EACL 2017)

• Structured Learning for Temporal Relation Extraction from Clinical Records (Leeuwenberg & Moens, EACL 2017)

ACL

• Artuur Leeuwenberg and Marie-Francine Moens. 2017. Structured Learning for Temporal Relation Extraction from Clinical Records. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1150–1158, Valencia, Spain. Association for Computational Linguistics.