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.- Anthology ID:
- 2020.wanlp-1.23
- Volume:
- Proceedings of the Fifth Arabic Natural Language Processing Workshop
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Imed Zitouni, Muhammad Abdul-Mageed, Houda Bouamor, Fethi Bougares, Mahmoud El-Haj, Nadi Tomeh, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 243–249
- Language:
- URL:
- https://aclanthology.org/2020.wanlp-1.23
- DOI:
- Cite (ACL):
- Ghoul Dhaou and Gaël Lejeune. 2020. Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 243–249, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification (Dhaou & Lejeune, WANLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.wanlp-1.23.pdf