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.
Artificial Intelligence in Arabic Natural Language Processing: A Review of Models, Datasets, and Applications
Artificial Intelligence
Natural Language Processing
Arabic Language
Transformer Models
Deep Learning
Sentiment Analysis.
Publication Date
Sat Dec 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
The Cluster Analysis by Using Nonparametric Cubic B-Spline Modeling for Longitudinal Data
البيانات الطولية
نموذج الشرائح B-spline التكعيبية اللامعلمية
التحليل العنقودي
طريقة الاتجاه المتناوب لخوارزمية المضاعف ADMM.
Longitudinal Data
Nonparametric Cubic B-Spline
Cluster Analysis
The Alternating Direction Method for Multiplier Algorithm ADMM.
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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