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    <titleInfo>
        <title>Lexicon information in neural sentiment analysis: a multi-task learning approach</title>
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    <name type="personal">
        <namePart type="given">Jeremy</namePart>
        <namePart type="family">Barnes</namePart>
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    <name type="personal">
        <namePart type="given">Samia</namePart>
        <namePart type="family">Touileb</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Lilja</namePart>
        <namePart type="family">Øvrelid</namePart>
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    <name type="personal">
        <namePart type="given">Erik</namePart>
        <namePart type="family">Velldal</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2019-sep–oct</dateIssued>
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            <title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Mareike</namePart>
            <namePart type="family">Hartmann</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Barbara</namePart>
            <namePart type="family">Plank</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Linköping University Electronic Press</publisher>
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                <placeTerm type="text">Turku, Finland</placeTerm>
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    <abstract>This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.</abstract>
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        <url>https://aclanthology.org/W19-6119</url>
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        <date>2019-sep–oct</date>
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            <start>175</start>
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%0 Conference Proceedings
%T Lexicon information in neural sentiment analysis: a multi-task learning approach
%A Barnes, Jeremy
%A Touileb, Samia
%A Øvrelid, Lilja
%A Velldal, Erik
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F barnes-etal-2019-lexicon
%X This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.
%U https://aclanthology.org/W19-6119
%P 175-186

Markdown (Informal)

[Lexicon information in neural sentiment analysis: a multi-task learning approach](https://aclanthology.org/W19-6119) (Barnes et al., NoDaLiDa 2019)

• Lexicon information in neural sentiment analysis: a multi-task learning approach (Barnes et al., NoDaLiDa 2019)

ACL

• Jeremy Barnes, Samia Touileb, Lilja Øvrelid, and Erik Velldal. 2019. Lexicon information in neural sentiment analysis: a multi-task learning approach. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 175–186, Turku, Finland. Linköping University Electronic Press.