Word Sense Induction is a task of automatically finding word senses from large scale texts. It is generally considered as an unsupervised clustering problem. This paper introduces a hypergraph model in which nodes represent instances of contexts where a target word occurs and hyperedges represent higher-order semantic relatedness among instances. A lexical chain based method is used for discovering the hyperedges, and hypergraph clustering methods are used for finding word senses among the context instances. Experiments show that this model outperforms other methods in supervised evaluation and achieves comparable performance with other methods in unsupervised evaluation.
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