## 4 Experimental Analysis and Results
### Experimental Setup  
The model uses AraBERT as the base encoder for the Character Representation Layer. Training is performed using the AdamW optimizer with a learning rate of \(5\times 10^{-5}\), a weight decay of \(1\times 10^{-2}\), and a batch size of 16 sentences. We trained the model for up to 10 epochs, applying early stopping to prevent overfitting. Experiments were conducted on an NVIDIA A100 GPU, with each epoch taking approximately 12 min. Table 3 shows the results of our model compared to state-of-the-art methods. As shown in Figure 2, the results of our model outperform all baselines in terms of F1-score and EMR. For instance, on ANERcorp, it achieves an F1-score of 93.0%, surpassing XLM-R + CRF by 2.4%. Similarly, on ACE 2005, the model's F1-score of 89.6% highlights its ability to handle nested entities effectively. While XLM-R excels in cross-lingual transfer, its lack of Arabic-specific morphological analyzers limits its ability to disambiguate diacritic-free text, underscoring the need for our tailored architecture. To comprehensively evaluate the performance of the proposed framework, we compared it with a combination of Arabic-specific and generalized state-of-the-art baselines. Specifically, we included AraBERT + CRF and XLM-R + CRF, which are commonly used for Arabic NER, as well as two recent generalized NER methods that have demonstrated strong performance across multiple languages: LUKE ([PERSON] et al. [21]) and Seq2 Seq Span-based NER ([PERSON] et al. [22]). LUKE employs entity-aware attention, making it particularly effective at capturing contextualized entity representations, while the Seq2 Seq Span-based NER formulates entity recognition as a sequence-to-sequence span generation task, enabling it to effectively handle nested and overlapping entities across languages. This diversified set of baselines allows for a robust assessment of the proposed model's strengths in both Arabic-specific and cross-lingual settings. Specifically, 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, making it particularly effective at capturing contextualized entity representations, while Seq2 Seq Span-based NER \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Exact Match (\%)** \\ \hline AraBERT + CRF & ANERcorp & 89.2 & 87.4 & 88.3 & 85.1 \\ \hline XLM-R + CRF & ANERcorp & 91.5 & 89.8 & 90.6 & 87.2 \\ \hline
**Proposed Model** & ANERcorp & 93.7 & 92.3 & 93.0 & 91.5 \\ \hline NestedNER & ACE 2005 & 85.6 & 82.9 & 84.2 & 80.4 \\ \hline GlobalPointer & ACE 2005 & 88.1 & 86.7 & 87.4 & 84.2 \\ \hline
**Proposed Model** & ACE 2005 & 90.2 & 89.1 & 89.6 & 86.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed model with state-of-the-art methods on ANERcorp and ACE 2005 datasets, demonstrating superior F1-score and exact match ratio (EMR). Figure 2: Performance comparison of the proposed model with state-of-the-art methods on ANERcorp and ACE 2005 datasets, highlighting improvements in F1-score and Exact Match Ratio (EMR). The proposed model outperforms baselines such as AraBERT + CRF and XLM-R + CRF, demonstrating superior recognition of both flat and nested entities. formulates entity recognition as a sequence-to-sequence span generation task, enabling it to effectively handle nested and overlapping entities across languages. Each chart in Figure 3 highlights the model's superiority in precision, recall, and F1-score, demonstrating its ability to handle both flat and nested entities effectively.