@inproceedings{dhaou-lejeune-2020-comparison,
    title = "Comparison between Voting Classifier and Deep Learning methods for {A}rabic Dialect Identification",
    author = {Dhaou, Ghoul  and
      Lejeune, Ga{\"e}l},
    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/ingest-emnlp/2020.wanlp-1.23/",
    pages = "243--249",
    abstract = "In this paper, we present three methods developed for the NADI shared task on Arabic Dialect Identification for tweets. The first and the second method use respectively a machine learning model based on a Voting Classifier with words and character level features and a deep learning model at word level. The third method uses only character-level features. We explored different text representation such as Tf-idf (first model) and word embeddings (second model). The Voting Classifier was the most powerful prediction model, achieving the best macro-average F1 score of 18.8{\%} and an accuracy of 36.54{\%} on the official test. Our model ranked 9 on the challenge and in conclusion we propose some ideas to improve its results."
}