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        <title>Normalization of Indonesian-English Code-Mixed Twitter Data</title>
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    <name type="personal">
        <namePart type="given">Anab</namePart>
        <namePart type="given">Maulana</namePart>
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    <name type="personal">
        <namePart type="given">Mirna</namePart>
        <namePart type="family">Adriani</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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    <abstract>Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5554</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Normalization of Indonesian-English Code-Mixed Twitter Data
%A Barik, Anab Maulana
%A Mahendra, Rahmad
%A Adriani, Mirna
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F barik-etal-2019-normalization
%X Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.
%R 10.18653/v1/D19-5554
%U https://aclanthology.org/D19-5554
%U https://doi.org/10.18653/v1/D19-5554
%P 417-424

Markdown (Informal)

[Normalization of Indonesian-English Code-Mixed Twitter Data](https://aclanthology.org/D19-5554) (Barik et al., WNUT 2019)

• Normalization of Indonesian-English Code-Mixed Twitter Data (Barik et al., WNUT 2019)

ACL

• Anab Maulana Barik, Rahmad Mahendra, and Mirna Adriani. 2019. Normalization of Indonesian-English Code-Mixed Twitter Data. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 417–424, Hong Kong, China. Association for Computational Linguistics.