A Survey of Word Sense Disambiguation: Applications, Approaches and Future Directions

Word Sense Disambiguation (WSD) is a foundational problem in Natural Language Processing (NLP) that involves determining the correct meaning of a word based on its context. As language is inherently ambiguous, resolving lexical ambiguities is critical for improving the performance of various NLP applications, including machine translation, information retrieval, sentiment analysis, and question-answering. This paper comprehensively surveys WSD, tracing its historical evolution from rule-based approaches to contemporary deep learning techniques. It discusses key methodologies such as knowledge-based, supervised, and unsupervised learning, highlighting their strengths, limitations, and applications. The importance of WSD is explored in diverse contexts, including bioinformatics, semantic web development, and lexicography. Despite significant advancements, challenges such as handling polysemous words, adapting to domain-specific contexts, and addressing resource limitations in low-resource languages persist. The paper also examines the integration of linguistic resources and computational techniques to enhance WSD systems and underscores future research directions to develop robust, scalable solutions. By addressing these challenges, WSD holds the potential to drive innovation across NLP applications, ensuring accurate semantic understanding and interpretation of human language.