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<Paper uid="P01-1069">
  <Title>Text Chunking using Regularized Winnow</Title>
  <Section position="5" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we described a text chunking system using regularized Winnow. Since regularized Winnow is robust to irrelevant features, we can construct a very high dimensional feature space and let the algorithm pick up the important ones.</Paragraph>
    <Paragraph position="1"> We have shown that state of the art performance can be achieved by using this approach. Furthermore, the method we propose is computationally more efficient than all other systems reported in the literature that achieved performance close to ours. Our system is also relatively simple which does not involve much engineering tuning. This means that it will be relatively easy for other researchers to implement and reproduce our results.</Paragraph>
    <Paragraph position="2"> Furthermore, the success of regularized Winnow in text chunking suggests that the method might be applicable to other NLP problems where it is necessary to use large feature spaces to achieve good performance.</Paragraph>
  </Section>
class="xml-element"></Paper>