Machine Learning-Based Approach for Arabic Dialect Identification

摘要

This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. There are four subtasks, two sub-tasks for country-level identification and the other two subtasks for province-level identification. The data in this task covers a total of 100 provinces from all 21 Arab countries and come from the Twitter domain. The proposed systems depend on five machine-learning approaches namely Complement Naieve Bayes, Support Vector Machine, Decision Tree, Logistic Regression and Random Forest Classifiers. F_1 macro-averaged score of Naieve Bayes classifier outperformed all other classifiers for development and test data.