@inproceedings{talafha-etal-2019-mawdoo3,
    title = "Mawdoo3 {AI} at {MADAR} Shared Task: {A}rabic Tweet Dialect Identification",
    author = "Talafha, Bashar  and
      Farhan, Wael  and
      Altakrouri, Ahmed  and
      Al-Natsheh, Hussein",
    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/ingest_wac_2008/W19-4629/",
    doi = "10.18653/v1/W19-4629",
    pages = "239--243",
    abstract = "Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84{\%} on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams."
}