Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words
This addresses the challenge of accurately interpreting polysemy words in natural language processing, but it appears incremental as it modifies an existing WordNet structure rather than introducing a fundamentally new approach.
The paper tackles the problem of Word Sense Disambiguation by developing a new WordNet model that organizes polysemy words and single-sense words based on clue words, using these clues to disambiguate meanings in context with knowledge-based algorithms.
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.