Paper: Word Sense Induction Disambiguation Using Hierarchical Random Graphs

ACL ID D10-1073
Title Word Sense Induction Disambiguation Using Hierarchical Random Graphs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extrac- tion and others. Most of the work in these ar- eas formulate their problem in a graph-based setting and apply unsupervised graph cluster- ing to obtain a set of clusters. Recent studies suggest that graphs often exhibit a hierarchi- cal structure that goes beyond simple flat clus- tering. This paper presents an unsupervised method for inferring the hierarchical group- ing of the senses of a polysemous word. The inferred hierarchical structures are applied to the problem of word sense disambiguation, where we show that our method performs sig- nificantly better than traditional graph-based methods and agglomerative cluste...