<![CDATA[Arabic Morphological ambiguity presents one of the most persistent obstacles in computational text analysis, stemming from the language's derivational architecture, the widespread omission of diacritical marks, and the capacity of a single root to generate dozens of surface forms sharing identical consonantal skeletons. This challenge is particularly acute in Arabic natural language processing, where accurate morphological interpretation underpins higher-level tasks such as machine translation, information retrieval, sentiment analysis, and automated text understanding. This study critically reviews how modern computational systems address morphological ambiguity in Arabic, tracing the evolution from rule-based analyzers to statistical models, and ultimately to deep learning and Large Language Models (LLMs). A descriptive-analytical approach was applied through a critical narrative review of foundational and contemporary literature in Arabic NLP. The findings indicate that while rule-based systems established morphological coverage, they lacked contextual disambiguation. Statistical systems achieved meaningful accuracy gains (exceeding 96% in POS tagging) by integrating supervised classification. Transformer-based architectures—particularly AraBERT, MARBERT, and CAMeLBERT—have since delivered state-of-the-art results by attending to broad contextual cues, with MARBERT showing superior performance in dialectal contexts. Recently, Arabic-specific LLMs such as AceGPT, Jais, and ALLaM have further pushed the boundaries of language understanding. Despite this progress, structural constraints persist: the scarcity of large, well-annotated dialectal corpora, the high computational cost of LLMs, and the persistent gap between Modern Standard Arabic (MSA) and real-world dialectal usage. The study concludes that advancing Arabic morphological processing requires coordinated development of linguistic resources, with direct and profound implications for Arabic language education and intelligent text analysis.]]>
