ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification
Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat · 2019
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.