ARABIC LANGUAGE AND DIGITAL TRANSFORMATION: A READING INTO THE IMPACT OF ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.52747/aqujall.5.01.529Keywords:
Arabic language processing, Artificial Intelligence, Digital innovation, Deep learning, Linguistic resourcesAbstract
This study aims to evaluate the current state of computational Arabic language processing within the context of digital innovation and the shift toward artificial intelligence models in natural language processing, through a systematic analysis of the structural properties that affect modeling efficiency. The study adopts a descriptive-analytical approach grounded in the decomposition of Arabic linguistic structure across its levels (morphological, syntactic, semantic, and written) and maps these levels onto the requirements of computational representation. The findings demonstrate that the absence of diacritical marking, the richness of the morphological system, the flexibility of syntactic ordering, the multiplicity of functional particles, and dialectal variation collectively constitute principal constraints that elevate the degree of ambiguity in Arabic text and adversely affect the accuracy of automated analysis. The study further reveals a relative deficiency in digital linguistic resources with respect to size, annotation, and quality, a deficiency that is directly reflected in model performance particularly in tasks involving semantic comprehension, classification, and translation. At the same time, the study documents the progress achieved by deep learning models in representing linguistic context, while affirming that their effectiveness in Arabic remains contingent upon the degree to which they accommodate the language's structural properties. On this basis, the study proposes an integrative approach that embeds Arabic linguistic knowledge, across its various levels, into the design of computational models, with the aim of improving accuracy, reducing ambiguity, and enhancing generalizability across contemporary language applications.
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