Paper: Word Sense Induction Using Lexical Chain based Hypergraph Model

ACL ID C14-1152
Title Word Sense Induction Using Lexical Chain based Hypergraph Model
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Word Sense Induction is a task of automatically finding word senses from large scale texts. It is general- ly 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 high- er-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 in- stances. Experiments show that this model outperforms other methods in supervised evaluation and achieves comparable performance with other methods in unsupervised evaluation.