Clustering for Clarity: Improving Word Sense Disambiguation through Multilevel Analysis

Abstract

In natural language processing, a critical activity known as word sense disambiguation (WSD) seeks to ascertain the precise meaning of an ambiguous word
in context. Traditional methods for WSD frequently involve supervised learning methods and lexical databases like WordNet. However, these methods fall
short in managing word meaning complexity and capturing fine-grained differences. In this paper, for increasing the precision and granularity of word sense
disambiguation we proposed multilevel clustering method that goes deeper in the nested levels as locate groups of linked context words and categorize them
according to their word meanings. With this method, we can more effectively manage polysemy and homonymy as well as detect minute differences in meaning.          An actual investigation of the SemCor corpus demonstrates the performance score of multilevel clustering in WSD. This proposed method successfully
separated clusters and groups context terms according to how semantically related they are, producing improved disambiguation outcomes. A more detailed
knowledge of word senses and their relationships may be obtained thanks to the clustering process, which makes it possible to identify smaller clusters inside larger clusters. The outcomes demonstrate how multilevel clustering may enhance the granularity and accuracy of WSD. Our solution overcomes the drawbacks of conventional approaches and provides a more fine-grained representation of word senses by combining clustering algorithms.