Word sense induction disambiguation using hierarchical random graphs

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others. Most of the work in these areas formulate their problem in a graph-based setting and apply unsupervised graph clustering to obtain a set of clusters. Recent studies suggest that graphs often exhibit a hierarchical structure that goes beyond simple flat clustering. This paper presents an unsupervised method for inferring the hierarchical grouping 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 significantly better than traditional graph-based methods and agglomerative clustering yielding improvements over state-of-the-art WSD systems based on sense induction.
Original languageEnglish
Title of host publicationEMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Number of pages10
Publication statusPublished - 2010

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