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<Paper uid="W97-0613">
  <Title>The &amp;quot;Casual Cashmere Diaper Bag&amp;quot;: Constraining Speech Recognition Using Examples</Title>
  <Section position="7" start_page="64" end_page="65" type="evalu">
    <SectionTitle>
6 Applicability and application of
</SectionTitle>
    <Paragraph position="0"> this approach 6.1 Where does this approach work? It is important to note that this approach depends on there being some simple way to indicate in a grammar what sort of &amp;quot;agreement&amp;quot; is required between the parts of a phrase, and that a relatively rich example set illustrating the &amp;quot;good&amp;quot; agreements also must be available. General language procesing lacks one or both of these requirements, so this approach must be understood as having relevance only where the ratio of example data is high relative to the variability that must be supported in the spoken language being processed.</Paragraph>
    <Paragraph position="1"> Our example case used a &amp;quot;binary&amp;quot; decision paradigm, completely ruling out combinations which did not match up with criteria from the example set; by using likelihood weighting instead of rigid exclusion, a more flexible system could be built.</Paragraph>
    <Section position="1" start_page="64" end_page="65" type="sub_section">
      <SectionTitle>
6.2 Semantic leverage
</SectionTitle>
      <Paragraph position="0"> Clearly some categorization of the items used in this technique improves both the simplicity and the generality of the restrictions that can be generated. I.e., if we use the know!edge that &amp;quot;diaper bag&amp;quot; and &amp;quot;book bag&amp;quot; are types of luggage, we can write the restriction rules to record and test the markings on their &amp;quot;type&amp;quot; rather than their &amp;quot;species&amp;quot;, and thus get information about the appropriate modifiers for &amp;quot;duf- null fel bag&amp;quot; without having ever &amp;quot;seen&amp;quot; sentences about duffel bags.</Paragraph>
    </Section>
  </Section>
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