<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="miller-etal-2017-unsupervised">
    <titleInfo>
        <title>Unsupervised Domain Adaptation for Clinical Negation Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Miller</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Steven</namePart>
        <namePart type="family">Bethard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hadi</namePart>
        <namePart type="family">Amiri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guergana</namePart>
        <namePart type="family">Savova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>BioNLP 2017</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada,</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.</abstract>
    <identifier type="citekey">miller-etal-2017-unsupervised</identifier>
    <identifier type="doi">10.18653/v1/W17-2320</identifier>
    <location>
        <url>https://aclanthology.org/W17-2320</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>165</start>
            <end>170</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Unsupervised Domain Adaptation for Clinical Negation Detection
%A Miller, Timothy
%A Bethard, Steven
%A Amiri, Hadi
%A Savova, Guergana
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F miller-etal-2017-unsupervised
%X Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.
%R 10.18653/v1/W17-2320
%U https://aclanthology.org/W17-2320
%U https://doi.org/10.18653/v1/W17-2320
%P 165-170

Markdown (Informal)

[Unsupervised Domain Adaptation for Clinical Negation Detection](https://aclanthology.org/W17-2320) (Miller et al., BioNLP 2017)

• Unsupervised Domain Adaptation for Clinical Negation Detection (Miller et al., BioNLP 2017)

ACL

• Timothy Miller, Steven Bethard, Hadi Amiri, and Guergana Savova. 2017. Unsupervised Domain Adaptation for Clinical Negation Detection. In BioNLP 2017, pages 165–170, Vancouver, Canada,. Association for Computational Linguistics.