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        <title>Modeling Global Syntactic Variation in English Using Dialect Classification</title>
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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="family">Zampieri</namePart>
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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">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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            <namePart type="given">Ahmed</namePart>
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    <abstract>This paper evaluates global-scale dialect identification for 14 national varieties of English on both web-crawled data and Twitter data. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.</abstract>
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%0 Conference Proceedings
%T Modeling Global Syntactic Variation in English Using Dialect Classification
%A Dunn, Jonathan
%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 dunn-2019-modeling
%X This paper evaluates global-scale dialect identification for 14 national varieties of English on both web-crawled data and Twitter data. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.
%R 10.18653/v1/W19-1405
%U https://aclanthology.org/W19-1405
%U https://doi.org/10.18653/v1/W19-1405
%P 42-53

Markdown (Informal)

[Modeling Global Syntactic Variation in English Using Dialect Classification](https://aclanthology.org/W19-1405) (Dunn, VarDial 2019)

• Modeling Global Syntactic Variation in English Using Dialect Classification (Dunn, VarDial 2019)

ACL

• Jonathan Dunn. 2019. Modeling Global Syntactic Variation in English Using Dialect Classification. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 42–53, Ann Arbor, Michigan. Association for Computational Linguistics.