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We apply machine learning techniques to study language transfer, a major topic in the theory of Second Language Acquisition (SLA). Using an SVM for the problem of native language classification, we show that a careful analysis of the effects of various features can lead to scientific insights. In particular, we demonstrate that character bigrams alone allow classification levels of about 66% for a 5-class task, even when content and function word differences are accounted for. This may show that native language has a strong effect on the word choice of people writing in a second language.

Using classifier features for studying the effect of native language on the choice of written second language words