@inproceedings{lichouri-abbas-2020-simple,
    title = "Simple vs Oversampling-based Classification Methods for Fine Grained {A}rabic Dialect Identification in {T}witter",
    author = "Lichouri, Mohamed  and
      Abbas, Mourad",
    editor = "Zitouni, Imed  and
      Abdul-Mageed, Muhammad  and
      Bouamor, Houda  and
      Bougares, Fethi  and
      El-Haj, Mahmoud  and
      Tomeh, Nadi  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.wanlp-1.24/",
    pages = "250--256",
    abstract = "In this paper, we present a description of our experiments on country-level Arabic dialect identification. A comparison study between a set of classifiers has been carried out. The best results were achieved using the Linear Support Vector Classification (LSVC) model by applying a Random Over Sampling (ROS) process yielding an F1-score of 18.74{\%} in the post-evaluation phase. In the evaluation phase, our best submitted system has achieved an F1-score of 18.27{\%}, very close to the average F1-score (18.80{\%}) obtained for all the submitted systems."
}