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.
REF suggest that learners' L1 has a strong effect on word choice in their L2 writings.
Using Classifier Features for Studying the Effect of Native Language on the Choice of Written Second Language Words
Workshop on Cognitive Aspects of Computational Language Acquisition
Computer Science