@inproceedings{chen-etal-2017-improving,
    title = "Improving Native Language Identification by Using Spelling Errors",
    author = "Chen, Lingzhen  and
      Strapparava, Carlo  and
      Nastase, Vivi",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/P17-2086/",
    doi = "10.18653/v1/P17-2086",
    pages = "542--546",
    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."
}