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<Paper uid="W06-1664">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Graph-based Word Clustering using a Web Search Engine</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised algorithm for word clustering based on a word similarity measure by web counts. Each pair of words is queried to a search engine, which produces a co-occurrence matrix. By calculating the similarity of words, a word co-occurrence graph is obtained. A new kind of graph clustering algorithm called Newman clustering is applied for efficiently identifying word clusters. Evaluations are made on two sets of word groups derived from a web directory and WordNet.</Paragraph>
  </Section>
class="xml-element"></Paper>