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<Paper uid="P97-1006">
  <Title>Document Classification Using a Finite Mixture Model</Title>
  <Section position="8" start_page="45" end_page="46" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> Let us conclude this paper with the following remarks: null 1. The primary contribution of this research is that we have proposed the use of the finite mixture model in document classification.</Paragraph>
    <Paragraph position="1"> 2. Experimental results indicate that our method of using the finite mixture model outperforms the method based on hard clustering of words.</Paragraph>
    <Paragraph position="2">  method when we use our current method of creating clusters.</Paragraph>
    <Paragraph position="3"> Our future work is to include: 1. comparing the various methods over the entire Reuters corpus and over other data bases, 2. developing better ways of creating clusters. Our proposed method is not limited to document classification; it can also be applied to other natural language processing tasks, like word sense disambiguation, in which we can view the context surrounding a ambiguous target word as a document and the word-senses to be resolved as categories.</Paragraph>
  </Section>
class="xml-element"></Paper>