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    <titleInfo>
        <title>N-gram and Neural Language Models for Discriminating Similar Languages</title>
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    <name type="personal">
        <namePart type="given">Andre</namePart>
        <namePart type="family">Cianflone</namePart>
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    <name type="personal">
        <namePart type="given">Leila</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>
            <namePart type="family">Zampieri</namePart>
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        <name type="personal">
            <namePart type="given">Liling</namePart>
            <namePart type="family">Tan</namePart>
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        <name type="personal">
            <namePart type="given">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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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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    <abstract>This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45% with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45% which is close to the accuracy of 89.38% achieved by the best submission.</abstract>
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%0 Conference Proceedings
%T N-gram and Neural Language Models for Discriminating Similar Languages
%A Cianflone, Andre
%A Kosseim, Leila
%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 cianflone-kosseim-2016-n
%X This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45% with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45% which is close to the accuracy of 89.38% achieved by the best submission.
%U https://aclanthology.org/W16-4831
%P 243-250

Markdown (Informal)

[N-gram and Neural Language Models for Discriminating Similar Languages](https://aclanthology.org/W16-4831) (Cianflone & Kosseim, VarDial 2016)

• N-gram and Neural Language Models for Discriminating Similar Languages (Cianflone & Kosseim, VarDial 2016)

ACL

• Andre Cianflone and Leila Kosseim. 2016. N-gram and Neural Language Models for Discriminating Similar Languages. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 243–250, Osaka, Japan. The COLING 2016 Organizing Committee.