Paper: Discriminating Non-Native English with 350 Words

ACL ID W13-1713
Title Discriminating Non-Native English with 350 Words
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2013
Authors

This paper describes MITRE?s participation in the native language identification (NLI) task at BEA-8. Our best effort performed at an ac- curacy of 82.6% in the eleven-way NLI task, placing it in a statistical tie with the best per- forming systems. We describe the variety of machine learning approaches that we ex- plored, including Winnow, language model- ing, 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 com- bining these base systems.