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Abstract
In today's digital landscape, Natural Language Processing (NLP) is the driving force behind efficient language interaction. Yet, for languages like Yoruba, with their intricate structures and tonal complexities, NLP presents unique challenges. This study's motivation lies in overcoming these hurdles by developing a dedicated NLP software for the Yoruba language, with a primary focus on navigating homographs, heterophones, and heteronyms – linguistic intricacies that have confounded language processors. The primary aim of this study is to design and implement an NLP software that adeptly manages Yoruba's homographic, heterophonic, and heteronymic characteristics. This goal is achieved through a multi-faceted approach that involves exploring the challenges presented by these linguistic phenomena, developing specialized software that can predict Yoruba words accurately, employing advanced algorithms for tone recognition, syllable identification, and linguistic analysis, and finally, rigorously evaluating the software's performance through comprehensive testing and user feedback. Data collection for this study encompassed a comprehensive Yoruba language corpus, drawing from various sources like texts, literature, and linguistic databases. The dataset was carefully curated to include words and phrases
