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<Paper uid="W00-0730">
  <Title>Use of ',Support Vector Learning for Chunk Identification</Title>
  <Section position="4" start_page="143" end_page="143" type="evalu">
    <SectionTitle>
4 Results
</SectionTitle>
    <Paragraph position="0"> We have applied our proposed method to the test data of CoNLL-2000 shared task, while training with the complete training data. For the kernel function, we use the 2-nd polynomial function. We set the beam width N to 5 tentatively. SVMs training is carried out with the SVM light package, which is designed and optimized to handle large sparse feature vector and large numbers of training examples(Joachims, 2000; Joachims, 1999a). It took about 1 day to train 231 classifiers with PC-Linux (Celeron 500Mhz, 512MB).</Paragraph>
    <Paragraph position="1"> Figure 1 shows the results of our experiments.</Paragraph>
    <Paragraph position="2"> The all the values of the chunking F-measure are almost 93.5. Especially, our method performs well for the chunk types of high frequency, such as NP, VP and PP.</Paragraph>
  </Section>
class="xml-element"></Paper>