## 2 Related Works
Despite significant advancements in Named Entity Recognition (NER), several gaps persist that hinder the development of robust and versatile models as shown in Table 1. Multilingual NER approaches, such as those by [PERSON] et al. [7] and [PERSON] et al. [8] achieve impressive results but often fail to address the unique challenges of morphologically rich languages like Arabic, particularly in informal and domain-specific contexts. Similarly, while [PERSON] et al. [10] enhanced Arabic NER by leveraging morphological features, their work focuses predominantly on formal texts, neglecting informal and diverse domains. Existing lexicon-enhanced methods, such as those by [PERSON] et al. [18] and [PERSON] et al. [16], highlight the importance of external knowledge but rely heavily on high-quality lexicons, which are scarce for many languages and domains. Furthermore, approaches like those of [PERSON] et al. [12] and [PERSON] et al. [17], though effective for nested and overlapping entities, lack scalability for large datasets and fail to integrate lexicons seamlessly into the learning process. To overcome the reliance on high-quality lexicons, [PERSON] et al. [16], and scalability issues in nested entity recognition, [PERSON] et al. [12], our framework integrates domain-specific lexicons via a noise-filtering GLU and employs efficient span-based encoding for hierarchical structures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year and Author** & **Focus Area** & **Technique** & **Dataset** & **Key Contribution** & **Limitations** \\ \hline [PERSON] et al., 2023 [7] & Multilingual NER & Transfer learning & MultiCoNER & Achieved state-of-the-art performance in 10 & Limited evaluation on morphologically rich languages. \\ [PERSON] et al., 2024 [8] & Cross-lingual NER & Contrastive learning & WikiAm & Enhanced NER for low-resource languages. & Does not address domain-specific challenges. \\ [PERSON] et al., & & Morphological & & Enhanced performance & Focused only on formal \\
2024 [10] & Arabic NER & feature & ANERcorp & using Arabic & Arabic texts, neglecting \\  & & integration & & morphology. & informal domains. \\ [PERSON] et al., 2023 [12] & Nested Entity & Span-based & & Superior nested entity & No specific adaptation for multilingual or Arabic \\  & Recognition & model & & recognition. & datasets. \\ [PERSON] et al., 2023 [16] & Cross-Attention & Cross-attention & & Improved contextual alignment. & Inefficient for large-scale \\  & for NER & modules & & & datasets due to \\ [PERSON] et al., 2023 [15] & Rare Entity & External lexicons & Low-resource & Recognized rare entities & Requires high-quality \\  & Recognition & & & & efficiently. \\ [PERSON] et al., 2023 [17] & Lexicon-enhanced NER & Lexicon-enhanced & Chinese NER & Improved performance & Limited application to other \\  & enhanced NER & transformers & & using lexical information. & morphologically complex \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent Named Entity Recognition (NER) studies, outlining their methodologies, key contributions, and limitations, particularly concerning challenges in morphologically rich languages like Arabic.