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<Paper uid="N01-1025">
  <Title>Chunking with Support Vector Machines</Title>
  <Section position="9" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Summary
</SectionTitle>
    <Paragraph position="0"> In this paper, we introduce a uniform framework for chunking task based on Support Vector Machines (SVMs). Experimental results on WSJ corpus show that our method outperforms other conventional machine learning frameworks such MBL and Maximum Entropy Models. The results are due to the good characteristics of generalization and nonoverfitting of SVMs even with a high dimensional vector space. In addition, we achieve higher accuracy by applying weighted voting of 8-SVM based systems which are trained using distinct chunk representations. null</Paragraph>
  </Section>
class="xml-element"></Paper>