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<Paper uid="W02-0811">
  <Title>Combining Heterogeneous Classifiers for Word-Sense Disambiguation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper discusses ensembles of simple but heterogeneous classifiers for word-sense disambiguation, examining the Stanford-CS224N system entered in the SENSEVAL-2 English lexical sample task. First-order classifiers are combined by a second-order classifier, which variously uses majority voting, weighted voting, or a maximum entropy model. While individual first-order classifiers perform comparably to middle-scoring teams' systems, the combination achieves high performance.</Paragraph>
    <Paragraph position="1"> We discuss trade-offs and empirical performance.</Paragraph>
    <Paragraph position="2"> Finally, we present an analysis of the combination, examining how ensemble performance depends on error independence and task difficulty.</Paragraph>
  </Section>
class="xml-element"></Paper>