An examination of algorithmic approaches to lexical disambiguation in Yoruba language texts
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- NGN 5000
Background of the Study
Lexical disambiguation plays a crucial role in natural language processing, particularly when addressing the complexities inherent in the Yoruba language. As digital content in Yoruba increases, effective computational techniques to resolve lexical ambiguities have become imperative for enhancing language comprehension and communication technologies. The Yoruba language is characterized by rich idiomatic expressions, tonal variations, and morphological intricacies that require tailored algorithmic solutions (Adegboye, 2023). Contemporary research has focused on integrating machine learning algorithms with rule-based approaches to capture contextual nuances and semantic variations. Such hybrid models have shown promise in improving precision in automated translation and information retrieval systems, thereby contributing to cultural preservation (Olajide, 2024). Advances in deep neural networks and statistical modeling have opened new avenues for addressing the challenges posed by homographs and polysemous words in Yoruba texts. However, issues such as the scarcity of annotated datasets and the inherent complexity of tonal systems continue to challenge developers. By exploring the evolution of algorithmic methods, this study aims to evaluate current techniques, identify gaps, and propose improvements that better align with the linguistic subtleties of Yoruba. Recent investigations underscore the importance of contextual embeddings and semantic networks in refining disambiguation outcomes, offering a promising direction for future research (Chukwu, 2025). The study thus situates itself at the intersection of computational linguistics and cultural preservation, ensuring that technological advancements respect and accurately represent linguistic diversity.
Statement of the Problem
Despite significant progress, existing algorithmic approaches for lexical disambiguation in Yoruba texts still face critical limitations. Current models often fail to accurately resolve ambiguities arising from tonal variations and context-dependent meanings, leading to misinterpretations in automated systems (Adegboye, 2023). The limited availability of comprehensive, annotated corpora exacerbates these challenges, restricting the development of robust hybrid models. Additionally, the rapid evolution of digital language use further complicates model adaptation and performance (Olajide, 2024). These issues hinder the integration of advanced natural language processing tools into educational and cultural preservation platforms. Therefore, there is a pressing need to identify and address these limitations to improve system accuracy and reliability. This study seeks to bridge these gaps by critically evaluating current methodologies and proposing novel solutions that enhance the disambiguation process for Yoruba language texts (Chukwu, 2025).
Objectives of the Study:
1. To evaluate existing algorithmic models for lexical disambiguation in Yoruba texts.
2. To identify challenges and gaps in current computational approaches.
3. To propose and test novel hybrid models that improve disambiguation accuracy.
Research Questions:
1. What are the limitations of current algorithmic approaches in processing Yoruba texts?
2. How can hybrid models enhance the accuracy of lexical disambiguation in Yoruba language processing?
3. What strategies can address the scarcity of annotated datasets for Yoruba?
Significance of the Study :
This study holds significant promise in advancing computational linguistics by addressing the unique challenges of Yoruba language processing. By improving algorithmic approaches to lexical disambiguation, the research can lead to enhanced natural language understanding and more effective communication technologies. The outcomes are expected to support educational initiatives, digital archiving, and cultural preservation. Furthermore, the findings may stimulate further research into hybrid computational models, thereby fostering innovation in language technology and broadening access to digital resources for Yoruba speakers (Adegboye, 2023; Olajide, 2024).
Scope and Limitations of the Study:
This study is confined to the exploration of algorithmic approaches for lexical disambiguation in Yoruba language texts, focusing on evaluating and developing computational models. It does not extend to other languages or broader NLP applications.
Definitions of Terms:
• Lexical Disambiguation: The process of resolving ambiguities in word meanings based on context.
• Algorithmic Approaches: Computational methods and models used to solve language processing problems.
• Hybrid Models: Systems that integrate rule-based and data-driven techniques for enhanced performance.
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