Abstract
This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 % and 62.12 % for both development and test sets.- Anthology ID:
- W19-4635
- Volume:
- Proceedings of the Fourth Arabic Natural Language Processing Workshop
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 269–273
- Language:
- URL:
- https://aclanthology.org/W19-4635
- DOI:
- 10.18653/v1/W19-4635
- Cite (ACL):
- Mourad Abbas, Mohamed Lichouri, and Abed Alhakim Freihat. 2019. ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 269–273, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification (Abbas et al., WANLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W19-4635.pdf