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<Paper uid="P06-2121">
  <Title>HAL-based Cascaded Model for Variable-Length Semantic Pattern Induction from Psychiatry Web Resources</Title>
  <Section position="10" start_page="951" end_page="951" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This study has presented an HAL-based cascaded model for variable-length semantic pattern induction. The HAL model provides an informative infrastructure for the CIP to induce semantic patterns from the unannotated psychiatry web corpora. Using the quality concepts and preserving the better results from the previous stage, the search space can be reduced to speed up the induction process. In addition, combining the relevance feedback and pseudo-relevance feedback, the induction process can be guided to induce more relevant semantic patterns. The experimental results demonstrated that our approach can not only reduce the reliance on annotated corpora but also obtain acceptable results with timelimited constraints. Future work will be devoted to investigating the detection of negative life events using the induced patterns so as to make the psychiatric services more effective.</Paragraph>
  </Section>
class="xml-element"></Paper>