SAHDA: A Semantic-Aware Hybrid Approach for Arabic Spell Checking with Domain Adaptation

Arabic spell checking faces inherent linguistic challenges due to its complex word structures and ambiguous spellings, where roots generate diverse forms and identical spellings mask multiple meanings. Current systems often prioritize technical accuracy over contextual understanding, struggling to preserve regional dialects or resolve semantic ambiguities. This paper introduces SAHDA, a hybrid framework that bridges computational methods with human linguistic expertise. By combining rule-based error detection, neural semantic analysis, and collaborative human-AI refinement, SAHDA addresses both structural errors and meaning-based ambiguities in written Arabic. Key innovations include context-aware dialect preservation to maintain regional linguistic diversity and adaptive learning techniques that enable efficient customization for specialized domains. Evaluations demonstrate SAHDA’s ability to resolve ambiguous terms and dialectal variations more effectively than existing approaches while minimizing overcorrection of valid regional expressions. As an open-source tool, the framework advances inclusive language technology by harmonizing standardization with dialectal richness— a critical advancement for supporting Arabic’s diverse written traditions in the digital age.