## 3 Materials and Methods  
This section describes the research design as it lays out strategies for optimal WSD outcomes. The researchers detail the model architecture, training implementation, and  
Figure 2: The architecture of Bi-GRU [47].  
evaluation methods, justifying each choice along the way. With this framework, this section presents a wide-angle view of how the design and implementation in this study developed and pushed forward existing WSD practices.  
This study uses baseline models depicted in Figure 3 for deep learning, such as LSTMLM, BiGRU, and RNN-LSTM. To enhance performance for context-aware disambiguation, the study also optimizes BiGRU using attention mechanisms and ADAM algorithms. Furthermore, the transformer models DeBERTa, RoBERTa, and ELECTRA, as well as pre-trained big language models, provide improved contextual awareness for WSD in morphologically rich languages like Sesotho and Leboa. Using ADAM and attention-based mechanisms, the study improved language models even further. By developing hybrid models that integrated BiGRU with transformer topologies, such as BiGRU + DeBERTa, BiGRU + RoBERTa, and BiGRU + ALBERT, the study expanded on its findings.  
This research tackles the challenges of polysemy by creating, executing, and validating a word sense disambiguation (WSD) system for Sesotho sa Leboa, a low-resource and morphologically complex language. The proposed solution integrates transformer architectures, corpus-based ensemble methods, and enhanced deep learning models, including Hybrid BiGRU-transformer techniques and attention mechanisms, to achieve superior semantic disambiguation performance. This research enhances the capabilities of natural language processing (NLP) for underrepresented languages and offers a versatile and scalable framework for additional computational and linguistic investigations.  
A recent advancement in natural language processing (NLP) has occurred, notably with transformer-based designs like BERT, GPT, and RoBERTa, which have yielded exceptionally competitive outcomes across various language comprehension tests [51]. Since these models are capable of capturing subtle details in contextual semantics, they can be used for WSD tasks requiring fine-grained sense distinction [56]. This work aims to augment WSD systems from a stronger, context-aware perspective [53]; it builds on these innovations with transformers.  
This research proposes an enhanced BiGRU architecture that incorporates additional mechanisms to better capture nuanced semantic relationships in WSD. By integrating elements such as task-specific fine-tuning and advanced contextual embeddings, the enhanced BiGRU aims to bridge the performance gap between traditional RNN-based architectures and transformer-based models. This modified BiGRU is specifically tailored to address  
Figure 3: Theoretical framework for WSD research methodology.  
the challenges posed by WSD, ensuring that both immediate and extended contextual information are effectively utilized for sense disambiguation.  
Building on these advancements, this methodology integrates transformer-based architectures with an enhanced BiGRU, designed to refine the model's interpretive accuracy in distinguishing subtle contextual variations in word meaning. Through this approach, this section seeks to contribute to the growing body of research focused on improving WSD outcomes, moving closer to achieving nuanced, accurate sense disambiguation across diverse language contexts.