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<Paper uid="W00-0730">
  <Title>Use of ',Support Vector Learning for Chunk Identification</Title>
  <Section position="5" start_page="143" end_page="143" type="concl">
    <SectionTitle>
5 Discussion
</SectionTitle>
    <Paragraph position="0"> In this paper, we propose Chunk identification analysis based on Support Vector Machines.</Paragraph>
    <Paragraph position="1"> Although we select features for learning in very straight way -- using all available features such as the words their POS tags without any cut-off threshold for the number of occurrence, we archive high performance for test data.</Paragraph>
    <Paragraph position="2">  When we use other learning methods such as Decision Tree, we have to select feature set manually to avoid over-fitting. Usually, these feature selection depends on heuristics, so that it is difficult to apply them to other classification problems in other domains.</Paragraph>
    <Paragraph position="3"> Memory based learning method can also ham dle all available features. However, the function to compute the distance between the test pattern and the nearest cases in memory is usually optimized in an ad-hoc way Through our experiments, we have shown the high generalization performance and high leature selection abilities of SVMs.</Paragraph>
  </Section>
class="xml-element"></Paper>