Discriminating Non-Native English with 350 Words
نویسندگان
چکیده
This paper describes MITRE’s participation in the native language identification (NLI) task at BEA-8. Our best effort performed at an accuracy of 82.6% in the eleven-way NLI task, placing it in a statistical tie with the best performing systems. We describe the variety of machine learning approaches that we explored, including Winnow, language modeling, logistic regression and maximum-entropy models. Our primary features were word and character n-grams. We also describe several ensemble methods that we employed for combining these base systems.
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