<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-etal-2018-modeling">
    <titleInfo>
        <title>Modeling Sentiment Association in Discourse for Humor Recognition</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Lizhen</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Donghai</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Song</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.</abstract>
    <identifier type="citekey">liu-etal-2018-modeling</identifier>
    <identifier type="doi">10.18653/v1/P18-2093</identifier>
    <location>
        <url>https://aclanthology.org/P18-2093</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>586</start>
            <end>591</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Modeling Sentiment Association in Discourse for Humor Recognition
%A Liu, Lizhen
%A Zhang, Donghai
%A Song, Wei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F liu-etal-2018-modeling
%X Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.
%R 10.18653/v1/P18-2093
%U https://aclanthology.org/P18-2093
%U https://doi.org/10.18653/v1/P18-2093
%P 586-591

Markdown (Informal)

[Modeling Sentiment Association in Discourse for Humor Recognition](https://aclanthology.org/P18-2093) (Liu et al., ACL 2018)

• Modeling Sentiment Association in Discourse for Humor Recognition (Liu et al., ACL 2018)

ACL

• Lizhen Liu, Donghai Zhang, and Wei Song. 2018. Modeling Sentiment Association in Discourse for Humor Recognition. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 586–591, Melbourne, Australia. Association for Computational Linguistics.