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BMSCE_ISE@INLI-FIRE-2017: A simple n-gram based approach for Native Language Identification

Native Language Identification (NLI) aims to identify native language L1 of an author by analysing the text written by him/her in other language L2. NLI is often implemented as a supervised classification problem. In this paper, we report a NLI system implemented using character tri-grams, word uni-grams and bigrams methods using linear classifier, Support Vector Machines (SVM). The work demonstrated is a participant of Indian Native Language Identification@FIRE 2017, achieving 0.27 overall accuracy for the corpus with 6 native languages. Furthermore, with subsequent evaluations, the best accuracy score obtained was 0.73 with 10 fold cross-validation on training data. We were able to achive above accuracy by incorporating uni-grams and bigrams of words.