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        <title>Language Identification and Analysis of Code-Switched Social Media Text</title>
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    <name type="personal">
        <namePart type="given">Deepthi</namePart>
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    <name type="personal">
        <namePart type="given">Suraj</namePart>
        <namePart type="family">Maharjan</namePart>
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            <title>Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching</title>
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            <namePart type="given">Gustavo</namePart>
            <namePart type="family">Aguilar</namePart>
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        <name type="personal">
            <namePart type="given">Fahad</namePart>
            <namePart type="family">AlGhamdi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Victor</namePart>
            <namePart type="family">Soto</namePart>
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            <namePart type="given">Mona</namePart>
            <namePart type="family">Diab</namePart>
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            <namePart type="given">Julia</namePart>
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    <abstract>In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset. In this regard, we build a new code-switched dataset for Hindi-English. To understand the code-switching patterns in these language pairs, we investigate different code-switching metrics. We find that the CRF model outperforms the neural network based models by a margin of 2-5 percentage points for Spanish-English and 3-5 percentage points for Hindi-English.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3206</identifier>
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%0 Conference Proceedings
%T Language Identification and Analysis of Code-Switched Social Media Text
%A Mave, Deepthi
%A Maharjan, Suraj
%A Solorio, Thamar
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F mave-etal-2018-language
%X In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset. In this regard, we build a new code-switched dataset for Hindi-English. To understand the code-switching patterns in these language pairs, we investigate different code-switching metrics. We find that the CRF model outperforms the neural network based models by a margin of 2-5 percentage points for Spanish-English and 3-5 percentage points for Hindi-English.
%R 10.18653/v1/W18-3206
%U https://aclanthology.org/W18-3206
%U https://doi.org/10.18653/v1/W18-3206
%P 51-61

Markdown (Informal)

[Language Identification and Analysis of Code-Switched Social Media Text](https://aclanthology.org/W18-3206) (Mave et al., ACL 2018)

• Language Identification and Analysis of Code-Switched Social Media Text (Mave et al., ACL 2018)

ACL

• Deepthi Mave, Suraj Maharjan, and Thamar Solorio. 2018. Language Identification and Analysis of Code-Switched Social Media Text. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 51–61, Melbourne, Australia. Association for Computational Linguistics.