〗In order to learn the higher-order semantic relatedness among multiple instance of target word,a hypergraph_model was proposed for word sense induction.First,a lexical chain based method was used for discovering the higher order semantic relatedness.Then a hypergraph was constructed,in which nodes represent the instances of contexts where a target word occurs,and hyperedges were formed by lexical chains.Finally,a maximum density based hypergraph clustering method was used for finding word senses. Experiments based on Semeval-2013 WSI task showed that this model gives an improvement of 5.6% and 6.4% in sense detection and sense ranking respectively, compared to the traditional graph model.
