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<Paper uid="P03-1005">
  <Title>Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper proposed the HDAG Kernel, which can reflect the richer information present within texts.</Paragraph>
    <Paragraph position="1"> Our proposed method is a very generalized framework for handling the structure inside a text.</Paragraph>
    <Paragraph position="2"> We evaluated the performance of the HDAG Kernel both as a similarity measure and as a kernel function. Our experiments showed that HDAG Kernel offers better performance than SSK, DSK, and the baseline method of the Cosine measure for feature vectors, because HDAG Kernel better utilizes the richer structure present within texts.</Paragraph>
  </Section>
class="xml-element"></Paper>