Literature Review: NLP Techniques for Arabic Dialect Recognition

This paper presents a review of Arabic dialect recognition within the domain of Natural Language Processing (NLP). We carefully examine a lot of recent studies that use various techniques, including both machine learning (ML) and deep learning (DL) methods. The studies we looked at include many ap-plications, ranging from opinion analysis and sentiment analysis to dialect identification, gender classification, and transliteration. Some of the main techniques used are Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), ensemble models, and sequence-tosequence architectures. These methodologies play a pivotal role in enhancing the accuracy and effectiveness of Arabic dialect analysis. The results of these studies show that the accuracy of Arabic dialect analysis varies based on the technique employed, the dataset used, and the specific task at hand. However, it is important to mention that deep learning techniques always achieve the highest levels of accuracy. In several instances, these techniques have provided accuracy rates higher than 90%, highlighting how well they work in handling the complexities of analyzing Arabic dialects.