Fewer features perform well at Native Language Identification task
Taraka Rama, Çağrı Çöltekin · 2017
This paper describes our results at the NLI shared task 2017.We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input.In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best.In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors.In the fusion task, we experimented with systems that used combination of i-vectors with higher order n-grams features, combination of i-vectors with word unigrams, a mean probability ensemble, and a stacked ensemble system.Our finding is that word unigrams in combination with i-vectors achieve higher score than systems trained with larger number of n-gram features.Our best-performing systems achieved F1scores of 87.16 %, 83.33 % and 91.75 % on the essay track, the speech track and the fusion track respectively.