PageRank-based Word Sense Induction within Web Search Results Clustering

Abstract

Word Sense Induction is an open problem in Natural Language Processing. Many recent works have been addressing this problem with a wide spectrum of strategies based on content analysis. In this paper, we present a sense induction strategy exclusively based on link analysis over the Web. In particular, we explore the idea that the main different senses of a given word share similar linking properties and can be found by performing clustering with link-based similarity metrics. The evaluation results show that PageRank-based sense induction achieves interesting results when compared to state-of-the-art content-based algorithms in the context of Web Search Results Clustering.