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<Paper uid="W03-1027">
  <Title>Virtual Examples for Text Classification with Support Vector Machines</Title>
  <Section position="9" start_page="6" end_page="6" type="concl">
    <SectionTitle>
6 Conclusion and Future Directions
</SectionTitle>
    <Paragraph position="0"> We have explored how virtual examples improve the performance of text classification with SVMs. For text classification, we have proposed methods to create virtual examples on the assumption that the label of a document is unchanged even if a small number of words are added or deleted. The experimental results have shown that our proposed methods improve the performance of text classification with SVMs, especially for small training sets. Although the proposed methods are not readily applicable to NLP tasks other than text classification, it is notable that the use of virtual examples, which has been very little studied in NLP, is empirically evaluated.</Paragraph>
    <Paragraph position="1"> In the future, it would be interesting to employ virtual examples with methods to use both labeled and unlabeled examples (e.g., (Blum and Mitchell, 1998; Nigam et al., 1998; Joachims, 1999)). The combined approach may yield better results with a small number of labeled examples. Another interesting direction would be to develop methods to create virtual examples for the other tasks (e.g., named entity recognition, POS tagging, and parsing) in NLP.</Paragraph>
    <Paragraph position="2"> We believe we can use prior knowledge on these tasks to create effective virtual examples.</Paragraph>
  </Section>
class="xml-element"></Paper>