Automated L1 identification in English learner essays and its implications for language transfer

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This article focuses on automatic text classification which aims at identifying the first language (L1) background of learners of English. A particular question arising in the context of automated L1 identification is whether any features that are informative for a machine learning algorithm relate to L1-specific transfer phenomena. In order to explore this issue further, we discuss the results of a study carried out in the wake of a Native Language Identification Task. The task is based on the TOEFL11 corpus (cf. Blanchard et al. 2013), which involves a sample of 12,100 essays written by participants in the TOEFL® test from 11 different language backgrounds (Arabic, Chinese, French, German, Hindi, Italian, Japanese, Korean, Spanish, Telugu, and Turkish). The article will show our results in automatic L1 detection in the TOEFL11 corpus. These results are discussed in light of relevant transfer features which turned out to be particularly informative for automatic detection of L1 German and L1 Italian.


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