<?xml version="1.0" standalone="yes"?>
<Paper uid="W95-0104">
  <Title>A Bayesian hybrid method for context-sensitive spelling correction</Title>
  <Section position="6" start_page="52" end_page="52" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> The work reported here builds on Yarowsky's use of decision lists to combine two component methods -- context words and collocations. Decision lists pool the evidence fl'om the two methods, and solve a target problem by applying the single strongest piece of evidence, whichever type that happens to be. This paper investigated the hypothesis that even better performance can be obtained by basing decisions on not just the single strongest piece of evidence, but on M1 available evidence. A method for doing this, based on Bayesian classifiers, was presented. It was applied to the task of context-sensitive spelling correction, and was found to outperform the component methods as well as decision lists. A comparison of the Bayesian hybrid method with Schabes's trigram-based method suggested a further combination in which trigrams would be used when the words in the confusion set had different parts of speech, and the Bayesian method would be used otherwise. This is a direction we plan to pursue in future research.</Paragraph>
  </Section>
class="xml-element"></Paper>