<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-hwa-2018-heuristically">
    <titleInfo>
        <title>Heuristically Informed Unsupervised Idiom Usage Recognition</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Changsheng</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rebecca</namePart>
        <namePart type="family">Hwa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-oct-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jun’ichi</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">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.</abstract>
    <identifier type="citekey">liu-hwa-2018-heuristically</identifier>
    <identifier type="doi">10.18653/v1/D18-1199</identifier>
    <location>
        <url>https://aclanthology.org/D18-1199</url>
    </location>
    <part>
        <date>2018-oct-nov</date>
        <extent unit="page">
            <start>1723</start>
            <end>1731</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Heuristically Informed Unsupervised Idiom Usage Recognition
%A Liu, Changsheng
%A Hwa, Rebecca
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-hwa-2018-heuristically
%X Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.
%R 10.18653/v1/D18-1199
%U https://aclanthology.org/D18-1199
%U https://doi.org/10.18653/v1/D18-1199
%P 1723-1731

Markdown (Informal)

[Heuristically Informed Unsupervised Idiom Usage Recognition](https://aclanthology.org/D18-1199) (Liu & Hwa, EMNLP 2018)

• Heuristically Informed Unsupervised Idiom Usage Recognition (Liu & Hwa, EMNLP 2018)

ACL

• Changsheng Liu and Rebecca Hwa. 2018. Heuristically Informed Unsupervised Idiom Usage Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1723–1731, Brussels, Belgium. Association for Computational Linguistics.