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<Paper uid="P06-1012">
  <Title>Estimating Class Priors in Domain Adaptation for Word Sense Disambiguation</Title>
  <Section position="8" start_page="95" end_page="95" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> Differences in sense priors between training and target domain datasets will result in a loss of WSD accuracy. In this paper, we show that using well calibrated probabilities to estimate sense priors is important. By calibrating the probabilities of the naive Bayes algorithm, and using the probabilities given by logistic regression (which is already well calibrated), we achieved significant improvements in WSD accuracy over previous approaches.</Paragraph>
  </Section>
class="xml-element"></Paper>