A Brief Review on Preprocessing Text in Arabic Language Dataset: Techniques and Challenges

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Ahmed Adil Nafea
Muhmmad Shihab Muayad
Russel R Majeed
Ashour Ali
Omar M. Bashaddadh
Meaad Ali Khalaf
Abu Baker Nahid Sami
Amani Steiti

Abstract

Text preprocessing plays an important role in natural language processing (NLP) tasks containing text classification, sentiment analysis, and machine translation. The preprocessing of Arabic text still presents unique challenges due to the language's rich morphology, complex grammar, and various character sets. This brief review studied various techniques utilized for preprocessing Arabic text data. This study discusses the challenges specific to Arabic text and current an overview of key preprocessing steps including normalization, tokenization, stemming, stop-word removal, and noise reduction. This survey analyzes preprocessing techniques on NLP tasks and focus on current research trends and future directions in Arabic text preprocessing.

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How to Cite
Nafea, A. A., Muayad, M. S., Majeed , R. R., Ali , A., Bashaddadh, O. M., Khalaf , M. A., Sami , A. B. N., & Steiti, A. (2024). A Brief Review on Preprocessing Text in Arabic Language Dataset: Techniques and Challenges. Babylonian Journal of Artificial Intelligence, 2024, 46–53. https://doi.org/10.58496/BJAI/2024/007
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