Exploring Syntactic Features for Native Language Identification: A Variationist Perspective on Feature Encoding and Ensemble Optimization.

In this paper, we systematically explore lexicalized and non-lexicalized local syntactic features for the task of Native Language Identification (NLI). We investigate different types of feature representations in single- and cross-corpus settings, including two representations inspired by a variationist perspective on the choices made in the linguistic system. To combine the different models, we use a probabilities-based ensemble classifier and propose a technique to optimize and tune it. Combining the best performing syntactic features with four types of n-grams outperforms the best approach of the NLI Shared Task 2013.