In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language.
['Moshe Koppel', 'Jonathan Schler', 'Kfir Zigdon']
Determining an author's native language by mining a text for errors