## 1 Introduction
However, it is also a real problem in modern NLP applications, since WSD has several problems with handling extremely polysemous terms. Thus, WSD calls for both theoretical and applied answers. As with other African languages, although several WSD solutions have been proposed for the English language, not much work has addressed the Sesotho sa Leboa language. This study addresses the challenge of improving NLP model performance by systematically evaluating baseline architectures and investigating optimization techniques and hybrid designs. It seeks to identify methods that maximize accuracy while balancing computational efficiency, ultimately advancing the development of robust NLP solutions. The absence of extensive data with word meaning annotations has frequently worsened this problem [11]. One of the most important tasks to determine whether a model is capable of deep comprehension is word sense disambiguation, and the emergence of pre-trained language models has led to significant progress in these tasks. Nevertheless, most pre-training activities used nowadays substitute the token level while ignoring the tokens' linguistic meaning. Therefore, it is debatable if learning the polysemy and disambiguating senses can be accomplished just through token-predicting approaches [12]. Researchers continue to explore multilingual ways to improve disambiguation accuracy [13]. WSD refers to the computational difficulty of determining a word's meaning in relation to its context, a problem sometimes described as AI-complete. Diverse methodologies for word sense disambiguation (WSD) have emerged, including supervised machine learning techniques that leverage extensive annotated corpora, dictionary-based strategies that utilize lexical knowledge bases, and unsupervised methods that infer word senses through clustering techniques [14; 15]. Among these, supervised learning methods have demonstrated the highest efficacy [16]. Research suggests that semi-supervised methods yield an accuracy similar to that of supervised techniques; nevertheless, neural networks, particularly convolutional neural networks (CNNs), exhibit superior effectiveness in natural language processing for word sense disambiguation (WSD) [17]. The absence of substantial corpora for low-resource languages, such as Sesotho sa Leboa (SsL), exacerbates the challenge of word sense disambiguation (WSD). While WSD approaches have been thoroughly examined for English, their application to morphologically rich, low-resource languages such as SsL poses distinct issues due to intricate linguistic structures. Resolving lexical ambiguity in SsL is crucial for enhancing machine translation and information retrieval efficacy in these languages. A global network was trained to disambiguate target words, attaining roughly 90% accuracy. In a separate study, [PERSON] et al. (2023) [18] devised and assessed Urdu semantic tagging approaches on a manually annotated corpus of 8000 tokens spanning multiple genres. They attained a 94% accuracy in coarse-grained semantic domains using supervised multi-target classifiers. Supervised word sense disambiguation techniques, encompassing algorithms such as neural networks, K-nearest neighbors, support vector machines (SVMs), decision trees, and Naive Bayes, utilize manually annotated data for the training of classification models [19; 20; 21]. Although effective, these methods necessitate considerable annotated corpora, rendering them resource-intensive. [PERSON] and [PERSON] (2016) [21] utilized a Naive Bayes classifier for word sense disambiguation in the Assamese language, attaining 71% accuracy with 160 ambiguous phrases sourced from WordNet and the Assamese Corpus. Hybrid learning approaches combine supervised, unsupervised, and knowledge-based techniques to enhance WSD performance. For instance, [PERSON] and [PERSON] (2022) [22] proposed a hybrid approach for Amharic WSD, which achieved an accuracy of 86% using a combination of supervised and unsupervised methods. Similarly, [PERSON] et al.