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<Paper uid="J90-1003">
  <Title>X and Y Separation Relation Word x Word y Mean Variance</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
WORD ASSOCIATION NORMS, \] /IUTUAL INFORMATION,
AND LEXICOGRAPHY
</SectionTitle>
    <Paragraph position="0"> The term word association is used in a very particular sense in the psycholinguistic literature. (Generally speaking, subjects respond quicker than normal to the word nurse if it follows a highly associated word such as doctor.) We will extend the term to provide the basis for a statistical description of a variety of interesting linguistic phenomena, ranging from semantic relations of the doctor/nurse type (content word/content word) to lexico-syntactic co-occurrence constraints between verbs and prepositions (content word/function word).</Paragraph>
    <Paragraph position="1"> This paper will propose an objective measure based on the information theoretic notion of mutual information, for estimating word association norms from computer readable corpora. (The standard method of obtaining word association norms, testing a few thousand :mbjects on a few hundred words, is both costly and unreliable.) The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.</Paragraph>
  </Section>
class="xml-element"></Paper>