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<Paper uid="P06-2054">
  <Title>Exploiting Non-local Features for Spoken Language Understanding</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modification is efficient and successful. We evaluate our method on an SLU task and show an error reduction of up to 27% over the base local model.</Paragraph>
  </Section>
class="xml-element"></Paper>