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.- Anthology ID:
- P17-2086
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 542–546
- Language:
- URL:
- https://aclanthology.org/P17-2086
- DOI:
- 10.18653/v1/P17-2086
- Cite (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.
- Cite (Informal):
- Improving Native Language Identification by Using Spelling Errors (Chen et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2086.pdf