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        <title>Representations of Language Varieties Are Reliable Given Corpus Similarity Measures</title>
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    <name type="personal">
        <namePart type="given">Jonathan</namePart>
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            <title>Proceedings of the Eighth 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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        <name type="personal">
            <namePart type="given">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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        <name type="personal">
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        <name type="personal">
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            <namePart type="given">Tommi</namePart>
            <namePart type="family">Jauhiainen</namePart>
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    <abstract>This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the web and tweets), allowing us to evaluate whether such geo-referenced corpora are reliable for modelling linguistic variation. The basic idea is that, if each source adequately represents a single underlying language variety, then the similarity between these sources should be stable across all languages and countries. The paper shows that there is a consistent agreement between these sources using frequency-based corpus similarity measures. This provides further evidence that digital geo-referenced corpora consistently represent local language varieties.</abstract>
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%0 Conference Proceedings
%T Representations of Language Varieties Are Reliable Given Corpus Similarity Measures
%A Dunn, Jonathan
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Scherrer, Yves
%Y Jauhiainen, Tommi
%S Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kiyv, Ukraine
%F dunn-2021-representations
%X This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the web and tweets), allowing us to evaluate whether such geo-referenced corpora are reliable for modelling linguistic variation. The basic idea is that, if each source adequately represents a single underlying language variety, then the similarity between these sources should be stable across all languages and countries. The paper shows that there is a consistent agreement between these sources using frequency-based corpus similarity measures. This provides further evidence that digital geo-referenced corpora consistently represent local language varieties.
%U https://aclanthology.org/2021.vardial-1.4
%P 28-38

Markdown (Informal)

[Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://aclanthology.org/2021.vardial-1.4) (Dunn, VarDial 2021)

• Representations of Language Varieties Are Reliable Given Corpus Similarity Measures (Dunn, VarDial 2021)

ACL

• Jonathan Dunn. 2021. Representations of Language Varieties Are Reliable Given Corpus Similarity Measures. In Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 28–38, Kiyv, Ukraine. Association for Computational Linguistics.