@inproceedings{rama-coltekin-2017-fewer,
    title = "Fewer features perform well at Native Language Identification task",
    author = {Rama, Taraka  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-5028/",
    doi = "10.18653/v1/W17-5028",
    pages = "255--260",
    abstract = "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."
}