Published Paper Details:

Word Sense Disambiguation (WSD) is a critical task in Natural Language Processing (NLP) that seeks to resolve lexical ambiguity by determining the appropriate meaning of a word within a specific context. While considerable progress has been made in WSD for high-resource languages, low-resource languages have received limited focus due to a lack of annotated datasets, lexical resources, and computational tools. This paper provides a thorough survey of current WSD techniques, including knowledge-based, supervised, unsupervised, and deep learning methods, and assesses their applicability in low-resource language environments. The study also addresses the challenges faced by low-resource languages, such as data scarcity, morphological complexity, the absence of standardized tools, and multilingual influences. Furthermore, it reviews WSD research in Indian languages, including Manipuri, Malayalam, Punjabi, Bengali, and Sindhi, to highlight both current advancements and limitations. Notably, Sindhi is recognized as one of the least investigated languages concerning WSD research. The findings of this survey underscore the necessity for developing resource-efficient and adaptable approaches, particularly leveraging machine learning and deep learning techniques, to enhance WSD performance in low-resource languages. The paper also outlines future research directions aimed at overcoming existing challenges and closing the research gap in this area.