Abstract
Arabic dialect identification (ADI) has recently drawn considerable interest among researchers in language recognition and natural language processing fields. This study investigated the use of a character-level model that is effectively unrestricted in its vocabulary, to identify fine-grained Arabic language dialects in the form of short written text. The Saudi dialects, particularly the four main Saudi dialects across the country, were considered in this study. The proposed ADI approach consists of five main phases, namely dialect data collection, data preprocessing and labelling, character-based feature extraction, deep learning character-based model/classical machine learning character-based models, and model evaluation performance. Several classical machine learning methods, including logistic regression, stochastic gradient descent, variations of the naive Bayes models, and support vector classification, were applied to the dataset. For the deep learning, the character convolutional neural network (CNN) model was adapted with a bidirectional long short-term memory approach. The collected data were tested under various classification tasks, including two-, three- and four-way ADI tasks. The results revealed that classical machine learning algorithms outperformed the CNN approach. Moreover, the use of the term frequency-inverse document frequency, combined with a character n-grams model ranging from unigrams to four-grams achieved the best performance among the tested parameters.