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        <title>Discriminating Similar Languages with Linear SVMs and Neural Networks</title>
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        <namePart type="given">Çağrı</namePart>
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            <title>Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)</title>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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        <name type="personal">
            <namePart type="given">Marcos</namePart>
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            <namePart type="given">Liling</namePart>
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            <namePart type="given">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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            <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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    <abstract>This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016. We submitted results of a single system based on support vector machines (SVM) with linear kernel and using character ngram features, which obtained the first rank at the closed training track for test set A. Besides the linear SVM, we also report additional experiments with a number of deep learning architectures. Despite our intuition that non-linear deep learning methods should be advantageous, linear models seems to fare better in this task, at least with the amount of data and the amount of effort we spent on tuning these models.</abstract>
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%0 Conference Proceedings
%T Discriminating Similar Languages with Linear SVMs and Neural Networks
%A Çöltekin, Çağrı
%A Rama, Taraka
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Tan, Liling
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%S Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F coltekin-rama-2016-discriminating
%X This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016. We submitted results of a single system based on support vector machines (SVM) with linear kernel and using character ngram features, which obtained the first rank at the closed training track for test set A. Besides the linear SVM, we also report additional experiments with a number of deep learning architectures. Despite our intuition that non-linear deep learning methods should be advantageous, linear models seems to fare better in this task, at least with the amount of data and the amount of effort we spent on tuning these models.
%U https://aclanthology.org/W16-4802
%P 15-24

Markdown (Informal)

[Discriminating Similar Languages with Linear SVMs and Neural Networks](https://aclanthology.org/W16-4802) (Çöltekin & Rama, VarDial 2016)

• Discriminating Similar Languages with Linear SVMs and Neural Networks (Çöltekin & Rama, VarDial 2016)

ACL

• Çağrı Çöltekin and Taraka Rama. 2016. Discriminating Similar Languages with Linear SVMs and Neural Networks. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 15–24, Osaka, Japan. The COLING 2016 Organizing Committee.