Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words

TL;DR

This paper introduces a new WordNet model that organizes senses of polysemy words by clue words to improve word sense disambiguation using knowledge-based algorithms.

Contribution

It proposes a novel WordNet structure that groups senses by clue words, enhancing disambiguation accuracy over traditional synset organization.

Findings

Improved disambiguation accuracy demonstrated

New model effectively organizes senses by clue words

Applicable to various parts of speech

Abstract

This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a new model of WordNet that organizes the different senses of polysemy words as well as the single sense words based on the clue words. These clue words for each sense of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the polysemy word in the given context using knowledge based Word Sense Disambiguation (WSD) algorithms. The clue word can be a noun, verb, adjective or adverb.