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<Paper uid="C00-1027">
  <Title>Empirical Estimates of Adaptation: The chance of Two Noriegas is closer to p/2 than p 2</Title>
  <Section position="5" start_page="185" end_page="185" type="concl">
    <SectionTitle>
8. Conclusions
</SectionTitle>
    <Paragraph position="0"> Adaptive language models were introduced to account for repetition. It is well known that the second instance of a word (or ngram) is nmch more likely than the first. But what we find surprising is just how large the effect is. The chance of two Noriegas is closer to p/2 than p 2.</Paragraph>
    <Paragraph position="1"> in addition to the magnitude of adaptation, we were also surprised by the shape: while the first instance of a word depends very strongly on frequency, the second does not. Adaptation depends more on content than flequency; adaptation is stronger for content words such as proper nouns, technical terminology and good keywords for information retrieval, and weaker for functioll words, cliches and first nalnes.</Paragraph>
    <Paragraph position="2"> The shape and magnitude of adaptation has implications for psycholinguistics, information retrieval and language modeling. Psycholinguistics has tended to equate word frequency with content, but our results suggest that two words with similar frequency (e.g., &amp;quot;Kennedy&amp;quot; and &amp;quot;except&amp;quot;) can be distinguished on the basis of their adaptation. Information retrieval has tended to use frequency in a similar way, weighting terms by IDF (inverse document frequency), with little attention paid to adaptation. We propose a term weighting method that makes use of adaptation (burstiness) and expansion frequency in a related paper (Umelnura and Church, submitted).</Paragraph>
    <Paragraph position="3"> Two estimation methods were introduced to demonstrate tile magnitude and shape of adaptation. Both methods produce similar results.</Paragraph>
    <Paragraph position="5"> Neighborhoods were then introduced for words such as &amp;quot;laid-off&amp;quot; that were not in the history but were close (&amp;quot;laid-off&amp;quot; is related to &amp;quot;layoff,&amp;quot; which was in the history). Neighborhoods were defined in terms of query expansion. The history is treated as a query in an information retriewd document-ranking system. Words in the k top-ranking documents (but not in the history) are called neighbors. Neighbors adapt more dmn other terms, but not as much as words that actually appeared in the history. Better neighbors (larger et) adapt more than worse neighbors (slnaller el).</Paragraph>
  </Section>
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