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        <title>Exploring Classifier Combinations for Language Variety Identification</title>
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    <name type="personal">
        <namePart type="given">Tim</namePart>
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        <namePart type="given">Walter</namePart>
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            <title>Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)</title>
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        <name type="personal">
            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jörg</namePart>
            <namePart type="family">Tiedemann</namePart>
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        <name type="personal">
            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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        <name type="personal">
            <namePart type="given">Ahmed</namePart>
            <namePart type="family">Ali</namePart>
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    <abstract>This paper describes CLiPS’s submissions for the Discriminating between Dutch and Flemish in Subtitles (DFS) shared task at VarDial 2018. We explore different ways to combine classifiers trained on different feature groups. Our best system uses two Linear SVM classifiers; one trained on lexical features (word n-grams) and one trained on syntactic features (PoS n-grams). The final prediction for a document to be in Flemish Dutch or Netherlandic Dutch is made by the classifier that outputs the highest probability for one of the two labels. This confidence vote approach outperforms a meta-classifier on the development data and on the test data.</abstract>
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%0 Conference Proceedings
%T Exploring Classifier Combinations for Language Variety Identification
%A Kreutz, Tim
%A Daelemans, Walter
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%Y Ali, Ahmed
%S Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F kreutz-daelemans-2018-exploring
%X This paper describes CLiPS’s submissions for the Discriminating between Dutch and Flemish in Subtitles (DFS) shared task at VarDial 2018. We explore different ways to combine classifiers trained on different feature groups. Our best system uses two Linear SVM classifiers; one trained on lexical features (word n-grams) and one trained on syntactic features (PoS n-grams). The final prediction for a document to be in Flemish Dutch or Netherlandic Dutch is made by the classifier that outputs the highest probability for one of the two labels. This confidence vote approach outperforms a meta-classifier on the development data and on the test data.
%U https://aclanthology.org/W18-3922
%P 191-198

Markdown (Informal)

[Exploring Classifier Combinations for Language Variety Identification](https://aclanthology.org/W18-3922) (Kreutz & Daelemans, VarDial 2018)

• Exploring Classifier Combinations for Language Variety Identification (Kreutz & Daelemans, VarDial 2018)

ACL

• Tim Kreutz and Walter Daelemans. 2018. Exploring Classifier Combinations for Language Variety Identification. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pages 191–198, Santa Fe, New Mexico, USA. Association for Computational Linguistics.