Papers › Fewer features perform well at Native Language Identification task

Fewer features perform well at Native Language Identification task

1 Sep 2017WS 2017 9archive 2025-07-28

Taraka Rama, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

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 F1-scores of 87.16{%}, 83.33{%} and 91.75{%} on the essay track, the speech track and the fusion track respectively.

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