<?xml version="1.0" standalone="yes"?>
<Paper uid="W02-1005">
  <Title>Augmented Mixture Models for Lexical Disambiguation</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We investigated the properties and performance of mixture models and two augmenting methods in an unified framework for Word Sense Disambiguation and Context-Sensitive Spelling Correction, showing experimentally that such joint models can successfully match and exceed the performance of feature-enhanced Bayesian models. The new classification correction method (MVC) we propose successfully addresses the problem of under-estimation of less likely classes, consistently and significantly improving the performance of the main mixture model across all tasks and languages. Finally, since the mixture model and its improvements performed well on two major tasks and several multilingual data sets, we believe that they can be productively applied to other related high-dimensionality lexical classification problems, including named-entity classification, topic classification, and lexical choice in machine translation.</Paragraph>
  </Section>
class="xml-element"></Paper>