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<Paper uid="C94-2165">
  <Title>CATCHING THE CHESHIRE CAT</Title>
  <Section position="7" start_page="1023" end_page="1024" type="concl">
    <SectionTitle>
CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="1023" end_page="1024" type="sub_section">
      <SectionTitle>
Possible usefulness
</SectionTitle>
      <Paragraph position="0"> The higher sensitivity to local constraints in the temporal ordering could be used in a parser for finding local phrases. This might also have its implications for language acquisition. It could be tested if language learners make mistakes that could be explained by the statistical connectivity between words. Further research is needed on how the measure of connectivity behaves on phrase boundaries.</Paragraph>
      <Paragraph position="1"> Areas where phrase finding could be useful include: text-to-speech (phrase intonation), machine translation (translation of compounds), and in information retrieval: phrase transfo~xnation of high frequency terms into medium fiequency telxns with a better discrimination value (Salton &amp; McGill, 1983).</Paragraph>
      <Paragraph position="2"> Characteristics The rt-measure is good at estimating global correlations in a document or collection of documents (Wettler &amp; Rapp, 1989). This could be used for capturing contextual and pragmatic constraints in a text. Other methods exist that are good, perhaps even better, at capturing for example synonymy.</Paragraph>
      <Paragraph position="3">  Linear least square mapping (Yang &amp; Chute 1992) is one method that has shown to bc promising on capturing very good mappings between, in their case, symptoms and diagnosis. The same technique could be used for mapping a text to its abstract. The draw-back of these methods is their inherent parallel structure which makes it hard to account for the ordering that natm'al language requires.</Paragraph>
      <Paragraph position="4"> The Ag-measure, on the other hand, is a local measure, that seems to capture dependencies in the temporal ordering of the language. It is hard to draw any definite conclusions from the analysis of only one text, but we have seen how the two proposed measures react 1o the frequencies of individual words, as well as the frequencies of word pairs. Taking into account the ability o1' Abt to find dependencies in the temporal ordering, we think it is a more relevaut measure than I-t for several aspects of natural language processing, but not all.</Paragraph>
    </Section>
  </Section>
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