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<Paper uid="P04-3021">
  <Title>Compiling Boostexter Rules into a Finite-state Transducer</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> Classi cation techniques have been used to effectively resolve ambiguity in many natural language 3For ease of exposition, we show the positive and negative sides of a rule each resulting in a context dependency rule.</Paragraph>
    <Paragraph position="1"> However, we can represent them in the form of a single context dependency rule which is ommitted here due to space constraints. null  processing tasks. However, most of these tasks have been solved in isolation and hence assume an un-ambiguous input. In this paper, we extend the utility of the classi cation based techniques so as to be applicable on packed representations such as word graphs. We do this by compiling the rules resulting from an AdaBoost classi er into a nite-state transducer. The resulting nite-state transducer can then be used as one part of a nite-state decoding chain.</Paragraph>
  </Section>
class="xml-element"></Paper>