<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="saito-etal-2019-minimally">
    <titleInfo>
        <title>Minimally Supervised Learning of Affective Events Using Discourse Relations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jun</namePart>
        <namePart type="family">Saito</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yugo</namePart>
        <namePart type="family">Murawaki</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kentaro</namePart>
            <namePart type="family">Inui</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jing</namePart>
            <namePart type="family">Jiang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vincent</namePart>
            <namePart type="family">Ng</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xiaojun</namePart>
            <namePart type="family">Wan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small.</abstract>
    <identifier type="citekey">saito-etal-2019-minimally</identifier>
    <identifier type="doi">10.18653/v1/D19-1581</identifier>
    <location>
        <url>https://aclanthology.org/D19-1581</url>
    </location>
    <part>
        <date>2019-11</date>
        <extent unit="page">
            <start>5758</start>
            <end>5765</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Minimally Supervised Learning of Affective Events Using Discourse Relations
%A Saito, Jun
%A Murawaki, Yugo
%A Kurohashi, Sadao
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F saito-etal-2019-minimally
%X Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small.
%R 10.18653/v1/D19-1581
%U https://aclanthology.org/D19-1581
%U https://doi.org/10.18653/v1/D19-1581
%P 5758-5765

Markdown (Informal)

[Minimally Supervised Learning of Affective Events Using Discourse Relations](https://aclanthology.org/D19-1581) (Saito et al., EMNLP-IJCNLP 2019)

• Minimally Supervised Learning of Affective Events Using Discourse Relations (Saito et al., EMNLP-IJCNLP 2019)

ACL

• Jun Saito, Yugo Murawaki, and Sadao Kurohashi. 2019. Minimally Supervised Learning of Affective Events Using Discourse Relations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5758–5765, Hong Kong, China. Association for Computational Linguistics.