Native Language Identification using probabilistic graphical models

Native Language Identification (NLI) is the task of identifying the native language of an author of a text written in a second language. Support Vector Machines and Maximum Entropy Learners are the most common methods used to solve this problem, but we consider it from the point-of-view of probabilistic graphical models. We hypothesize that graphical models are well-suited to this task, as they can capture feature inter-dependencies that cannot be exploited by SVMs. Using progressively more connected graphical models, we show that these models out-perform SVMs on reduced feature sets. Furthermore, on full feature sets, even naïve Bayes increases accuracy from 82.06% to 83.41% over SVMs on a 5-language classification task.