Artificial Intelligence in Arabic Natural Language Processing: A Review of Models, Datasets, and Applications

Abstract

Arabic language processing with artificial intelligence has evolved significantly in the past decades, from traditional rule- and dictionary-based techniques, through statistical models to modern deep and transformer models. This review intends to present an overview of the most well-known Arabic models as well as datasets used for its training, and the main practical applications such as sentiment analysis, machine translation, speech recognition, and smart assistant.

AI-based Arabic NLP has had good progress in the previous decades, from rule and dictionary-based approaches to statistical methods and deep transformative learning models nowadays. In addition to it, the most popular state-of-the-art models that are fine-tuned for the Arabic language and their corpora of training data will be considered as well as major applications such as Sentiment Analysis, Machine Translation, Speech Recognition, and Virtual Assistant.

This article review outlines the necessity for investment in language resources and advanced models to improve AI systems’ ability to accurately understand Arabic natural language, a contribution that will support real-life applications and smart services associated with its present formalized variant of AI model capabilities.