<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zou-etal-2018-lexicon">
    <titleInfo>
        <title>A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yicheng</namePart>
        <namePart type="family">Zou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tao</namePart>
        <namePart type="family">Gui</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qi</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xuanjing</namePart>
        <namePart type="family">Huang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. To achieve the above target, in this work, we propose a novel lexicon-based supervised attention model (LBSA), which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations. Compared with general attention models, our model has better interpretability and less noise. Experimental results on three large-scale sentiment classification datasets showed that the proposed method outperforms previous methods.</abstract>
    <identifier type="citekey">zou-etal-2018-lexicon</identifier>
    <location>
        <url>https://aclanthology.org/C18-1074</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>868</start>
            <end>877</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis
%A Zou, Yicheng
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F zou-etal-2018-lexicon
%X Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. To achieve the above target, in this work, we propose a novel lexicon-based supervised attention model (LBSA), which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations. Compared with general attention models, our model has better interpretability and less noise. Experimental results on three large-scale sentiment classification datasets showed that the proposed method outperforms previous methods.
%U https://aclanthology.org/C18-1074
%P 868-877

Markdown (Informal)

[A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis](https://aclanthology.org/C18-1074) (Zou et al., COLING 2018)

• A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (Zou et al., COLING 2018)

ACL

• Yicheng Zou, Tao Gui, Qi Zhang, and Xuanjing Huang. 2018. A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis. In Proceedings of the 27th International Conference on Computational Linguistics, pages 868–877, Santa Fe, New Mexico, USA. Association for Computational Linguistics.