<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="goldwasser-zhang-2016-understanding">
    <titleInfo>
        <title>Understanding Satirical Articles Using Common-Sense</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Goldwasser</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.</abstract>
    <identifier type="citekey">goldwasser-zhang-2016-understanding</identifier>
    <identifier type="doi">10.1162/tacl_a_00116</identifier>
    <location>
        <url>https://aclanthology.org/Q16-1038</url>
    </location>
    <part>
        <date>2016</date>
        <detail type="volume"><number>4</number></detail>
        <extent unit="page">
            <start>537</start>
            <end>549</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Understanding Satirical Articles Using Common-Sense
%A Goldwasser, Dan
%A Zhang, Xiao
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F goldwasser-zhang-2016-understanding
%X Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.
%R 10.1162/tacl_a_00116
%U https://aclanthology.org/Q16-1038
%U https://doi.org/10.1162/tacl_a_00116
%P 537-549

Markdown (Informal)

[Understanding Satirical Articles Using Common-Sense](https://aclanthology.org/Q16-1038) (Goldwasser & Zhang, TACL 2016)

• Understanding Satirical Articles Using Common-Sense (Goldwasser & Zhang, TACL 2016)

ACL

• Dan Goldwasser and Xiao Zhang. 2016. Understanding Satirical Articles Using Common-Sense. Transactions of the Association for Computational Linguistics, 4:537–549.