@inproceedings{eltanbouly-etal-2019-simple,
    title = {Simple But Not Na{\"i}ve: Fine-Grained {A}rabic Dialect Identification Using Only N-Grams},
    author = "Eltanbouly, Sohaila  and
      Bashendy, May  and
      Elsayed, Tamer",
    editor = "El-Hajj, Wassim  and
      Belguith, Lamia Hadrich  and
      Bougares, Fethi  and
      Magdy, Walid  and
      Zitouni, Imed  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-4624/",
    doi = "10.18653/v1/W19-4624",
    pages = "214--218",
    abstract = "This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic. Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies. We opted to adopt a very simple approach in terms of extracted features and classification models; we only utilize word and character n-grams as features, and Na {\ensuremath{\ddot{}}}{\i}ve Bayes models as classifiers. Surprisingly, the simple approach achieved non-na {\ensuremath{\ddot{}}}{\i}ve performance. The official results, reported on a held-out testing set, show that the dialect of a given sentence can be identified at an accuracy of 64.58{\%} by our best submitted run."
}