Language transfer, the characteristic second language usage patterns caused by native language interference, is investigated by Second Language Acquisition (SLA) researchers seeking to find overused and underused linguistic features. In this paper we develop and present a methodology for deriving ranked lists of such features. Using very large learner data, we show our method's ability to find relevant candidates using sophisticated linguistic features. To illustrate its applicability to SLA research, we formulate plausible language transfer hypotheses supported by current evidence. This is the first work to extend Native Language Identification to a broader linguistic interpretation of learner data and address the automatic extraction of underused features on a per-native language basis.
The authors of REF propose a different methodology, using linear SVM weights to extract lists of overused and underused linguistic features per L1 group.
Language Transfer Hypotheses with Linear SVM Weights
EMNLP
Computer Science