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<Paper uid="P01-1049">
  <Title>Building Semantic Perceptron Net for Topic Spotting</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7. Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we proposed an approach to automatically build semantic perceptron net (SPN) for topic spotting. The SPN is a connectionist model in which context is used to select the exact meaning of a word. By analyzing the context and co-occurrence statistics, and by looking up thesaurus, it is able to group the distributed but semantic related words together to form basic semantic nodes. Experiments on Reuters 21578 show that, to some extent, SPN is able to capture the semantics of topics and it performs well on topic spotting task.</Paragraph>
    <Paragraph position="1"> It is well known that human expert, whose most prominent characteristic is the ability to understand text documents, have a strong natural ability to spot topics in documents. We are, however, unclear about the nature of human cognition, and with the present state-of-art natural language processing technology, it is still difficult to get an in-depth understanding of a text passage. We believe that our proposed approach provides a promising compromise between full understanding and no understanding.</Paragraph>
  </Section>
class="xml-element"></Paper>