## 1 Introduction  
The Bantu language of the Sotho group is called Sesotho sa Leboa. Disjunctive orthography, particularly the category of verb prefixal morphemes, is what distinguishes the language. The language is regarded as semi-conjunctive since, in it, suffixal morphemes are written in a conjunctive manner. Additionally, because of its phonology and history, the Bantu language is regarded as agglutinative and has significant intrinsic structural similarities, yet it differs significantly in its orthography [1,2]. When polysemous words occur in sentences during human discourse, listeners can infer the meaning of the words based on the context. Since computers lack the skills and knowledge necessary to understand such sentences, a method is needed for disambiguation. In Sesotho sa Leboa language processing, WSD is the most alluring and difficult research subject. It is also incredibly significant for text processing and language understanding applications. In this study, the researchers offer methods for removing confusing Sesotho sa Leboa terms from sentences. Because of their straightforward morphological characteristics, English and other European languages have been investigated in a considerable number of studies published in the field of WSD [3]. Lexical ambiguity represents both homonymy and polysemy. Ambiguity is a form of polysemy or multiple meanings. One word or lexeme may have different distinctions of senses. Polysemy refers to one word with two or more meanings, whereas homonymy refers to two or more words with related meanings spelled identically and with the same pronunciation or sound. Distinguishing between polysemy and homonymy can be challenging as the boundary between them is fluid; however, the two are very distinct [4, 5, 6]. One of the main problems in natural language processing (NLP) is word sense disambiguation (WSD), which becomes more difficult in languages with limited resources like Sesotho sa Leboa. Sesotho sa Leboa lacks the extensive annotated corpora, strong linguistic resources, and pre-trained models required for efficient computational linguistic applications, in contrast to highly resourced languages like English. In addition to addressing the difficulties presented by WSD in Sesotho sa Leboa, this study intends to further the area of computational linguistics in low-resource languages. This WSD research is essential for closing the digital language gap caused by the growing demand for NLP solutions in native African languages. This work aims to improve the accessibility of linguistic technology and encourage the inclusion of Sesotho sa Leboa in the global NLP environment by creating strong, context-aware models. Languages are gradually acquired and formed via practice, production, social networking, and usage in daily life; as a result, word definitions must always accurately reflect objective reality [7]. Common nouns, entities, and modifiers in natural languages can suggest several ways to express the same idea, yet distinct terms in formal languages, once disambiguated, may align with a concept articulated in another formal language [8]. Spoken by about 4.7 million people, Sesotho sa Leboa, commonly referred to as \"Sepedi\" or \"Northern Sotho\", is one of South Africa's 11 official languages. Thirty dialects make up Sesotho sa Leboa, some of which notably differ from the standardized written form [6]. Words in Sesotho sa Leboa have many meanings, making it a highly polysemous language. Part-of-Speech (POS), specialized, metaphoric, and other types of polysemy exist in Sesotho sa Leboa, and each presents a unique computational problem for the delivery of WSD solutions. In current computational linguistics research, polysemy is still an unresolved subject that presents significant computational challenges [9]. Handling sense distinctiveness and vagueness vs. polysemy concerns remains an unresolved theoretical subject, as shown by the shortcomings of several polysemy tests. Thus, polysemy is still a theoretically unresolved topic [10].