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<Paper uid="N06-1061">
  <Title>Language Model-Based Document Clustering Using Random Walks</Title>
  <Section position="7" start_page="484" end_page="484" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have presented a language model inspired approach to document clustering. Our results show that even the simplest version of our approach with nearly no parameter tuning can outperform traditional a1a2 a3a4a5a2 models by a wide margin. Random walk iterations on our graph-based model have improved our results even more. Based on the success of our model, we will investigate various graph-based relationships for explaining semantic structure of text collections in the future. Possible applications include information retrieval, text clustering/classification and summarization.</Paragraph>
  </Section>
class="xml-element"></Paper>