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    <titleInfo>
        <title>Improving Native Language Identification by Using Spelling Errors</title>
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    <name type="personal">
        <namePart type="given">Lingzhen</namePart>
        <namePart type="family">Chen</namePart>
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        <namePart type="given">Carlo</namePart>
        <namePart type="family">Strapparava</namePart>
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    <name type="personal">
        <namePart type="given">Vivi</namePart>
        <namePart type="family">Nastase</namePart>
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        <dateIssued>2017-07</dateIssued>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
            <namePart type="family">Kan</namePart>
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    <abstract>In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error features lead to 1.2% improvement in accuracy on classifying texts in the TOEFL11 corpus by the author’s native language, compared to systems participating in the NLI shared task.</abstract>
    <identifier type="citekey">chen-etal-2017-improving</identifier>
    <identifier type="doi">10.18653/v1/P17-2086</identifier>
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%0 Conference Proceedings
%T Improving Native Language Identification by Using Spelling Errors
%A Chen, Lingzhen
%A Strapparava, Carlo
%A Nastase, Vivi
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F chen-etal-2017-improving
%X In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error features lead to 1.2% improvement in accuracy on classifying texts in the TOEFL11 corpus by the author’s native language, compared to systems participating in the NLI shared task.
%R 10.18653/v1/P17-2086
%U https://aclanthology.org/P17-2086
%U https://doi.org/10.18653/v1/P17-2086
%P 542-546

Markdown (Informal)

[Improving Native Language Identification by Using Spelling Errors](https://aclanthology.org/P17-2086) (Chen et al., ACL 2017)

• Improving Native Language Identification by Using Spelling Errors (Chen et al., ACL 2017)

ACL

• Lingzhen Chen, Carlo Strapparava, and Vivi Nastase. 2017. Improving Native Language Identification by Using Spelling Errors. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 542–546, Vancouver, Canada. Association for Computational Linguistics.