## 2 Related Works  
Named Entity Recognition (NER) has seen remarkable progress in recent years, driven by advancements in pre-trained language models, attention mechanisms, and domain-specific adaptations. Multilingual NER has gained significant attention, as highlighted by [PERSON] et al. [7], who leveraged transfer learning to achieve state-of-the-art performance across 10 languages, underscoring the importance of adapting models dynamically to target languages. Similarly, [PERSON] et al. [8] introduced a cross-lingual framework employing contrastive learning, significantly improving NER for low-resource languages by aligning multilingual embeddings. These studies reveal the potential of transfer learning and cross-lingual techniques to bridge linguistic gaps in NER. For domain-specific applications, [PERSON] et al. [9] showcased the effectiveness of integrating external knowledge graphs in biomedical NER, enabling precise recognition of specialized entities. In the Arabic NER domain, [PERSON] et al. [10] demonstrated the superiority of AraBERT over generic multilingual models by incorporating morphological features, a critical advancement given Arabic's rich morphology. Further emphasizing domain specificity, [PERSON] et al. [11] introduced dual-attention mechanisms that enhanced feature integration in financial texts, achieving substantial accuracy gains. These studies collectively highlight that combining domain knowledge with linguistic adaptations is crucial for advancing NER in specialized fields. The complexity of nested and overlapping entities remains a persistent challenge in NER. [PERSON] et al. [12] addressed this by proposing a span-based model with enhanced positional encoding, achieving notable success in nested entity recognition. Complementing this, [PERSON] et al. [13] developed a global attention mechanism that effectively tackled overlapping entity boundaries. Building on these innovations, [PERSON] et al. [14] proposed data augmentation techniques, such as back-translation and masking, which proved invaluable for underrepresented languages and noisy datasets. Arabic NER, in particular, faces unique challenges due to its complex morphology and diacritic-based ambiguity. [PERSON] et al. [15] showed that integrating morphological analyzers significantly improved recognition in informal texts, such as social media. Similarly, [PERSON] et al. [16] enhanced rare entity recognition by incorporating lexicons, a strategy that is particularly relevant for resource-constrained languages like Arabic. Additionally, [PERSON] et al. [17] demonstrated that cross-attention mechanisms improve contextual alignment, which is crucial for disambiguating morphologically similar tokens in Arabic. Despite these advancements, gaps remain in seamlessly combining morphological understanding, lexicon integration, and cross-lingual knowledge transfer for NER. Studies such as those of [PERSON] et al. [18] and [PERSON] et al. [19] underline the potential of integratinglexical features directly into transformer layers, while [PERSON] et al. [20] highlighted the promise of zero-shot and few-shot learning frameworks for expanding NER capabilities in languages lacking annotated datasets. These approaches emphasize the importance of scalability and adaptability in addressing resource limitations. In summary, recent studies underscore the growing emphasis on multilinguality, domain adaptation, and morphological complexity in NER. While advancements in cross-lingual learning and domain-specific models have pushed the boundaries of performance, the integration of lexicon features, morphological analyzers, and attention mechanisms remains a fertile ground for further exploration. Arabic NER, with its unique linguistic challenges, stands to benefit significantly from these emerging methodologies. Future work must focus on harmonizing these innovations to create robust, adaptable, and resource-efficient models for diverse applications.