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<Paper uid="P98-1016">
  <Title>Redundancy: helping semantic disambiguation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Redundancy is a good thing, at least in a learning process. To be a good teacher you must say what you are going to say, say it, then say what you have just said. Well, three times is better than one. To acquire and learn knowledge from text for building a lexical knowledge base, we need to find a source of information that states facts, and repeats them a few times using slightly different sentence structures. A technique is needed for gathering information from that source and identify the redundant information. The extraction of the commonality is an active learning of the knowledge expressed.</Paragraph>
    <Paragraph position="1"> The proposed research is based on a clustering method developed by Barri~re and Popowich (1996) which performs a gathering of related information about a particular topic. Individual pieces of information are represented via the Conceptual Graph (CG) formalism and the result of the clustering is a large CG embedding all individual graphs. In the present paper, we suggest that the identification of the redundant information within the resulting graph is very useful for disambiguation of the original information at the semantic level.</Paragraph>
  </Section>
class="xml-element"></Paper>