Voting Classifier vs Deep learning method in Arabic Dialect Identification

Dhaou Ghoul (1, 2) , Gaël Lejeune (3, 2)

1 STIH - Sens, Texte, Informatique, Histoire
2 STIH-LC - Équipe Linguistique computationnelle
3 STIH - Sens, Texte, Informatique, Histoire

In this paper, we present three methods developed by the SORBONNE Team 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 the 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.

Domaines: Traitement du texte et du document

Dhaou Ghoul, Gaël Lejeune. Voting Classifier vs Deep learning method in Arabic Dialect Identification. : Proceedings of the Fifth Arabic Natural Language Processing Workshop, COLING 2020, Dec 2020, Barcelone, Spain. ⟨hal-03089957⟩