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<Paper uid="W02-1115">
  <Title>Selecting the Most Highly Correlated Pairs within a Large Vocabulary</Title>
  <Section position="9" start_page="0" end_page="0" type="concl">
    <SectionTitle>
13 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper describes a method for selecting correlated pairs in a0a8a1a4a3a15a14 memory space and a0a2a1a4a3 a5 a7a10a9a12a11 a1a4a3a17a14a16a14 computation time, where a3 is the number of documents in a corpus, provided that there is an upper boundary in the number of different words/labels in one document/record. We have observed that a corpus usually has this kind of upper boundary, and have shown that we can uses a sequential file for most of our memory requirements.</Paragraph>
    <Paragraph position="1"> This method is useful not only for confidence but also for other functions whose values are decided by a21a23a22 a24 , a21a29a22a25a28 , a21a23a22 a30 , a21a23a22a12a32 . Examples of these functions are mutual information, the dice coefficient, the confidence measure, the phi coefficient and the complimentary similarity measure.</Paragraph>
  </Section>
class="xml-element"></Paper>