## 3 Materials and Methods
### Data Collection and Annotation  
To compile an SsL WSD dataset, data were collected from several authentic sources, including academic dissertations, research papers, dictionaries, and web pages specifically containing ambiguous words. These sources were selected due to their standardized usage of language, which serves as a basis for reliable word meanings. Content extraction was achieved through web scraping techniques using Beautiful Soup, which enabled the automated collection of text by parsing HTML and filtering out any HTML tags. This extraction focused on gathering contextual examples of ambiguous words, ensuring that the collected text represented natural, everyday language usage. The dataset for this study was created using web scraping techniques from a variety of sources because structured linguistic resources for Sesotho sa Leboa are scarce. These methods also included extracting articles from newspapers and blogs to capture linguistic diversity in the real world, gathering informal and conversational texts through social media and forums to include a variety of word senses and usages, and making sure that formal language samples from academic and government publications are of high quality for domain-specific disambiguation. During the manual annotation phase of the Sesotho sa Leboa dataset, two linguists with expertise in Sesotho sa Leboa morphology, syntax, and semantics contributed their professional insights on intricate linguistic structures and cases of ambiguity. Additionally, three native speakers of Sesotho sa Leboa from various dialectal backgrounds worked as a team to confirm contextual appropriateness and genuine language usage. Two master's students from the Applied Language Department also ensured quality by monitoring consistency and settling disagreements regarding annotations. This manual approach was essential for maintaining consistency and accuracy across the dataset, creating a dependable benchmark for WSD model development. Researchers use these types of annotated datasets to explore patterns in ambiguity and observe how words vary in meaning depending on their context within a sentence. A taxonomy of Sesotho sa Leboa lexical ambiguities is shown in Figure 4. Polysemy and homonymy were used in this study to identify ambiguity in the dataset. Both homonymy and polysemy are represented by lexical ambiguity. One type of polysemy is ambiguity regarding multiple meaning. A single word or lexeme may have multiple unique senses. Moreover, homonymy refers to two or more words with similar meanings that are spelled the same way and have the same pronunciation or sound. Although it can be challenging to distinguish between polysemy and homonymy because the line between the two is blurry, they are significantly different [4; 54; 57; 56]. Specifically, polysemy is well defined as a specific type of lexical ambiguity where a word or phrase has multiple semantically connected meanings that share the same etymology and fall into three sub-categories: metonymy, specialization polysemy, or metaphors [54; 57]. Again, it is possible for two lexemes with different etymologies to be written and pronounced similarly. The many forms of polysemy share a common ancestor, and it is possible to distinguish between basic and derived senses whenever they are hypothesized. To put it another way, a term is considered polysemous if and only if it has two or more senses. For instance, the word \"leleme\" can imply \"language\", \"tongue\", or \"telling lies\". Homonymy occurs when a word's meanings are unrelated, which also suggests that words do not create certain structures. For instance, the word \"lewa\" might signify \"spread of divine bones\", \"cooked corn\", or \"cave\". The detailed characteristics of the SsL WSD dataset are presented in Table 1, which summarizes essential metrics, including the total number of sentences, tokens, annotations, sense types, lemmas, and ambiguity level.