A study of N-gram and Embedding Representations for Native Language Identification
Sowmya Vajjala, Sagnik Banerjee · 2017
We report on our experiments with Ngram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU).Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features.We explored n-grams covering word, character, POS and word-POS mixed representations for this task.For embedding based feature representations, we employed both word and document embeddings.We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.