## 1 Introduction  
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that involves identifying and classifying entities, such as names of persons, locations, organizations, and events, in textual data. Despite significant advancements in NER for widely studied languages like English, Arabic remains a particularly challenging domain due to its morphological richness, orthographic ambiguity, and frequent occurrences of nested and overlapping entities [1]. While nested entities are common across many languages, Arabic texts present additional complexities due to the language's rich morphology, inflectional nature, and frequent compounding of entity phrases. This leads to highly ambiguous boundaries and nested structures that intertwine multiple entity types. For example, in the phrase \" \" \" \" \" \" \" \" \" \" \" (King Abdullah Medical City), the text contains nested entities (a location name containing a person name) that cannot be easily separated using traditional sequence labeling techniques. Another critical limitation of existing Arabic NER systems relates to their handling of external lexical information. Current methods often rely heavily on static lexicons or general-purpose pre-trained language models. While these resources capture valuable semantic and syntactic information, they also introduce noise in the form of irrelevant matches, outdated terms, or domain mismatches, especially in specialized texts such as biomedical reports or informal texts from social media [2]. This inability to dynamically filter external lexical information leads to suboptimal performance, especially when models encounter domain shifts or noisy, ambiguous content. Additionally, Arabic text often omits diacritical marks, which are essential for disambiguating words with identical spellings but different meanings [3]. This orthographic ambiguity further compounds the difficulty in recognizing entities correctly. Existing NER approaches for Arabic, particularly those adapted from other languages, tend to either overfit to the training domain or generalize poorly across diverse contexts, particularly when external lexical cues are either incomplete or contradictory. In this work, we propose a novel Arabic NER framework that directly addresses these challenges through innovative architectural enhancements. The framework introduces a Hybrid Feature Fusion Layer, which integrates external lexical features using cross-attention mechanisms combined with a Gated Lexical Unit (GLU) to filter noise and retain relevant information [4]. Additionally, a Compound Span Representation Layer is designed to capture hierarchical relationships and dependencies between entities using Rotary Positional Encoding (RoPE) and Bidirectional GRUs. Finally, the Enhanced Multi-Label Classification Layer enables the model to disambiguate overlapping spans and assign multiple entity types where necessary. Despite these challenges, recent advancements in pre-trained language models, such as AraBERT and multilingual models like XLM-R, have shown promise in Arabic NLP tasks [5]. These models leverage large-scale text corpora to capture contextual and semantic information effectively. However, their general-purpose architectures are not specifically optimized for the unique requirements of Arabic NER, such as handling nested entities and filtering irrelevant lexical information [6]. As a result, there remains a significant performance gap that motivates the development of specialized models. The proposed framework is evaluated on three datasets: ANERcorp, ACE 2005, and a custom biomedical dataset. These datasets encompass a wide range of scenarios, from flat entities in ANERcorp to complex nested structures in ACE 2005 and specialized domain entities in the biomedical dataset [2]. The results demonstrate that the proposed model consistently outperforms state-of-the-art methods in precision, recall, and F1-score, highlighting its robustness and adaptability across diverse domains.