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<Paper uid="W97-0111">
  <Title>Clustering Co-occurrence Graph based on Transitivity</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Clustering is the operation to group words by some criterion. Thesauri and synonym dictionaries are some of its manual examples. Automatic outputs can be used not only to revise them, but also to aid ambiguity resolution, an essential problem in natural language processing. For instance, the me~ing of an ambiguous word can be decided by e.xamln'i~g the duster it belongs to. Furthermore, clusters grouped according to topics have many application areas such as automatic document classification. The input in this paper is the word co-occurrence graph obta~ued from corpus. The output is its subgraphs with the condition that each sub-graph is specialized in a topic.</Paragraph>
    <Paragraph position="1"> Many automatic clustering methods have been already proposed. Most of them are based on the statistical similarity between two words.</Paragraph>
    <Paragraph position="2"> Our approach is different; it is graph theoretical. We tried to find out the special structure in linguistic graph.</Paragraph>
    <Paragraph position="3"> Having a huge co-occurrence graph obtained from a corpus, we first tried to decompose it to analyze its graph structure using graph theoretical tools, such as maximum strongly connected components, or biconnected components. Although both tools decompose a graph into tightly connected subgraphs, these trials resulted in vain. The question arose; what must be taken into account to decompose the co-occurrence graph. 7 The answer is the ambiguity. Furthermore, we reached to the conclusion that the ambiguity can be explained in terms of intransitivity. This feature is developed into an algorithm for clustering.</Paragraph>
    <Paragraph position="4"> This paper is organized as follows. The following chapter describes the relationship between the transitivity in the graph and the ambiguity resolution. Chapter 3 shows the relationships between clustering and transitivity. Chapter 4 proposes and discusses an algorithm for clustering. Related work is resumed in Chapter 5. Our method is examined in Chapter 6 by some experiments.</Paragraph>
  </Section>
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