## 4 Experimental Analysis and Results
### Experimental Setup
#### 4.3.1 Comparison with Basic Models  
The proposed framework consistently outperforms all baselines across the three datasets: ANERcorp, ACE 2005, and the custom biomedical dataset as shown in Table 4. The improvements are particularly significant in the ACE 2005 dataset, which contains complex nested entities, demonstrating the proposed model's superior capability to capture hierarchical structures in Arabic text. Compared to LUKE and Seq2 Seq Span-based NER, the proposed framework outperforms both LUKE and Seq2 Seq Span-based NER across the ANERcorp, ACE 2005, and custom biomedical datasets, highlighting its ability to better capture Arabic morphological and contextual nuances. The improvements are  
Figure 3: Comparative performance of the proposed model relative to Arabic-specific baselines (AraBERT + CRF and XLM-R + CRF) and generalized state-of-the-art methods (LUKE and Seq2 Seq Span-based NER) across two datasets: ANERcorp and ACE 2005. The figure presents a side-by-side comparison of precision, recall, and F1-score, highlighting the proposed model’s superior ability to handle both flat and nested entity recognition challenges. particularly noticeable in nested entity scenarios, where language-specific adaptations play a crucial role. To illustrate, Figure 3 presents a comprehensive comparative analysis of the proposed model's performance relative to both Arabic-specific and generalized state-of-the-art baselines across two datasets: ANERcorp and ACE 2005. The Arabic-specific baselines include AraBERT + CRF and XLM-R + CRF, while the generalized baselines consist of LUKE and Seq2 Seq Span-based NER, both of which have demonstrated strong performance across multiple languages. LUKE employs entity-aware attention, which enhances its ability to capture contextualized entity representations, while Seq2 Seq Span-based NER formulates entity recognition as a sequence-to-sequence span generation task, making it particularly effective for nested entities. Each chart in Figure 3 highlights the superior precision, recall, and F1-score of the proposed model, demonstrating its robustness and adaptability to varying entity recognition challenges. On ANERcorp, the proposed model achieves an F1-score of 93.0%, significantly outperforming XLM-R + CRF by 2.4 percentage points, and further surpassing LUKE and Seq2 Seq NER, confirming its effectiveness in handling flat entities. Similarly, on ACE 2005, which contains complex nested entities, the proposed model excels with an F1-score of 89.6%, outperforming all other baselines, including the generalized LUKE and Seq2 Seq NER models. These results validate the contributions of the Hybrid Feature Fusion Layer and Compound Span Representation Layer, which enable effective handling of Arabic's morphological complexity and nested entity structures. The clear margin of improvement across both datasets underscores the robustness, adaptability, and efficiency of the proposed architecture in addressing real-world NER tasks in Arabic. To evaluate the contributions of each novel component, we conducted an ablation study by removing one component at a time. The results are summarized in Table 5 and show that each component significantly contributes to the model's performance. Removing the Hybrid Feature Fusion Layer results in a 2.1% drop in F1-score, demonstrating its importance in integrating high-quality lexical features. Similarly, the absence of the Compound Span Representation Layer reduces the F1-score by 3%, highlighting its role in handling nested and overlapping entities. The Enhanced Multi-Label Classification Layer also plays a critical role in assigning correct labels to spans, with its removal causing a 1.5% decrease in F1-score.