Paper: Word Sense Discrimination By Clustering Contexts In Vector And Similarity Spaces

ACL ID W04-2406
Title Word Sense Discrimination By Clustering Contexts In Vector And Similarity Spaces
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

This paper systematically compares unsuper- vised word sense discrimination techniques that cluster instances of a target word that oc- cur in raw text using both vector and similarity spaces. The context of each instance is repre- sented as a vector in a high dimensional fea- ture space. Discrimination is achieved by clus- tering these context vectors directly in vector space and also by finding pairwise similarities among the vectors and then clustering in sim- ilarity space. We employ two different repre- sentations of the context in which a target word occurs. First order context vectors represent the context of each instance of a target word as a vector of features that occur in that con- text. Second order context vectors are an indi- rect representation of the context based on the a...