Improving Native Language Identification by Using Spelling Errors

Lingzhen Chen, Carlo Strapparava, Vivi Nastase


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P17-2086.pdf