Clustering for clarity: improving word sense disambiguation through multilevel analysis

Abstract

ExistingWord Sense Disambiguation (WSD) techniques have limits in exploring word-context relationships since they only deal with the effective use of word embedding, lexical-based information via WordNet, or the precision of clustering algorithms. In order to overcome this limitation, the study suggests a unique hybrid methodology that makes use of context embedding based on center-embedding in order to capture semantic subtleties and utilizing with a two-level k-means clustering algorithm. Such generated context embedding, producing centroids that serve as representative points for semantic information inside clusters. Additionally, go with such captured cluster- centres in the nested levels of clustering process, locate groups of linked context words and categorize them according to their word meanings that effectively manage polysemy/ homonymy as well as detect minute differences in meaning. Our proposed approach offers a substantial improvement over traditional WSD methods by harnessing the power of center-embedding in context representation, enhancing the precision of clustering and ultimately overcoming existing limitations in context-based sense disambiguation.