Refining Word Sense Disambiguation via Multilevel Clustering of Lexical Representations

Prof. Omar F. Abdelrahman; Laila K. Mostafa

Authors

Prof. Omar F. Abdelrahman Department of Computer Engineering, Cairo University, Cairo, Egypt
Laila K. Mostafa Department of Computer Engineering, Cairo University, Cairo, Egypt

Keywords:

Word sense disambiguation, multilevel clustering, lexical representations, natural language processing

Abstract

Word Sense Disambiguation (WSD), the task of identifying the correct meaning of a word in a given context, remains a pivotal challenge in Natural Language Processing (NLP). This article explores the application of multilevel clustering techniques to enhance WSD accuracy and provide clearer contextual understanding. By leveraging hierarchical analysis across various linguistic features, including word embeddings, lexical networks, and syntactic patterns, this approach aims to capture intricate semantic relationships. We discuss the methodological framework for integrating clustering at different analytical levels and synthesize the potential benefits, particularly in addressing polysemy and homonymy. The proposed multilevel clustering paradigm offers a robust pathway for refining sense assignments, leading to improved performance in downstream NLP applications.

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