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<Paper uid="C96-2157">
  <Title>A Self-Learning Universal Concept Spotter</Title>
  <Section position="13" start_page="935" end_page="935" type="concl">
    <SectionTitle>
11 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we presented the Universal Spotter, a system that learns to spot in-text references to instances of a given semantic class: people, organizations, products, equipment, tools, to nmne just a few. A specific class spotter is created through an unsupervised learning process on a text corpus given only an initial nser-supplied seed: either a number of examples of the concept, or a typical context in which they can be found. The experiment shows that this method indeed can produce useflfl spotters based on easy-to-construct seeds. Tile results shown here are promising, can be further improved by using lexicon verification.</Paragraph>
    <Paragraph position="1"> Different methods of computing SWs, combining SWs, and parameter adjustmenting for the bootstrapping process need to be explored as we believe there is still room for improvement. The method is being continuously refined as we gain more feedback from empirical tests across several different applications.</Paragraph>
    <Paragraph position="2"> We believe that tile Universal Spotter can replace much of the need to create hand-crafted concept spotters commonly used in text extraction operations. In can also be applied to building other than the most common spotters such as those for people names, place names, or company names. In fact, is can be used to create more-or-less on-demand spotters, depending upon the applications and its subject domain. In particular, we believe such spotters will be required to gain further advance in intelligent text indexing and retrieval applications, text summarization, and database apI)lications, e.g., (Harman, 1995), (Strzalkowski, 1995).</Paragraph>
  </Section>
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