Department of Computer Engineering2024-11-0920139781-9372-8449-7N/A2-s2.0-85040566343N/Ahttps://hdl.handle.net/20.500.14288/6571Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013.Computer scienceArtificial intelligenceConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85040566343&partnerID=40&md5=512c8f1ed3ceb85d3172ea3e57f5d3d9798