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        <title>Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers</title>
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    <name type="personal">
        <namePart type="given">Adrien</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>The present contribution revolves around efficient approaches to language classification which have been field-tested in the Vardial evaluation campaign. The methods used in several language identification tasks comprising different language types are presented and their results are discussed, giving insights on real-world application of regularization, linear classifiers and corresponding linguistic features. The use of a specially adapted Ridge classifier proved useful in 2 tasks out of 3. The overall approach (XAC) has slightly outperformed most of the other systems on the DFS task (Dutch and Flemish) and on the ILI task (Indo-Aryan languages), while its comparative performance was poorer in on the GDI task (Swiss German dialects).</abstract>
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%0 Conference Proceedings
%T Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers
%A Barbaresi, Adrien
%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 barbaresi-2018-computationally
%X The present contribution revolves around efficient approaches to language classification which have been field-tested in the Vardial evaluation campaign. The methods used in several language identification tasks comprising different language types are presented and their results are discussed, giving insights on real-world application of regularization, linear classifiers and corresponding linguistic features. The use of a specially adapted Ridge classifier proved useful in 2 tasks out of 3. The overall approach (XAC) has slightly outperformed most of the other systems on the DFS task (Dutch and Flemish) and on the ILI task (Indo-Aryan languages), while its comparative performance was poorer in on the GDI task (Swiss German dialects).
%U https://aclanthology.org/W18-3918
%P 164-171

Markdown (Informal)

[Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers](https://aclanthology.org/W18-3918) (Barbaresi, VarDial 2018)

• Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers (Barbaresi, VarDial 2018)

ACL

• Adrien Barbaresi. 2018. Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pages 164–171, Santa Fe, New Mexico, USA. Association for Computational Linguistics.