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Improving Native Language Identification with TF-IDF weighting

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons4454

Gebre,  Binyam Gebrekidan
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons216

Wittenburg,  Peter
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

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Volltexte (frei zugänglich)

W13-1728.pdf
(Verlagsversion), 136KB

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Zitation

Gebre, B. G., Zampieri, M., Wittenburg, P., & Heskes, T. (2013). Improving Native Language Identification with TF-IDF weighting. In Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 216-223).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-FB4D-B
Zusammenfassung
This paper presents a Native Language Identification (NLI) system based on TF-IDF weighting schemes and using linear classifiers - support vector machines, logistic regressions and perceptrons. The system was one of the participants of the 2013 NLI Shared Task in the closed-training track, achieving 0.814 overall accuracy for a set of 11 native languages. This accuracy was only 2.2 percentage points lower than the winner's performance. Furthermore, with subsequent evaluations using 10-fold cross-validation (as given by the organizers) on the combined training and development data, the best average accuracy obtained is 0.8455 and the features that contributed to this accuracy are the TF-IDF of the combined unigrams and bigrams of words.