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        <title>The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task</title>
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    <name type="personal">
        <namePart type="given">Adrian-Gabriel</namePart>
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            <title>Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects</title>
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            <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">Shervin</namePart>
            <namePart type="family">Malmasi</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">Ahmed</namePart>
            <namePart type="family">Ali</namePart>
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    <abstract>This article presents the model that generated the runs submitted by the R2I_LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.</abstract>
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%0 Conference Proceedings
%T The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task
%A Chifu, Adrian-Gabriel
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Malmasi, Shervin
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Ali, Ahmed
%S Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2019
%8 June
%I Association for Computational Linguistics
%C Ann Arbor, Michigan
%F chifu-2019-r2i
%X This article presents the model that generated the runs submitted by the R2I_LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.
%R 10.18653/v1/W19-1414
%U https://aclanthology.org/W19-1414
%U https://doi.org/10.18653/v1/W19-1414
%P 138-143

Markdown (Informal)

[The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task](https://aclanthology.org/W19-1414) (Chifu, VarDial 2019)

• The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task (Chifu, VarDial 2019)

ACL

• Adrian-Gabriel Chifu. 2019. The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 138–143, Ann Arbor, Michigan. Association for Computational Linguistics.