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<Paper uid="W02-1004">
  <Title>Modeling Consensus: Classifier Combination for Word Sense Disambiguation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
BD
</SectionTitle>
    <Paragraph position="0"> (C6 is the number of classifiers) by combination, if the classifiers' errors are uncorrelated and unbiased.</Paragraph>
    <Paragraph position="1"> The target task studied here is word sense disambiguation in the SENSEVAL evaluation framework (Kilgarriff and Palmer, 2000; Edmonds and Cotton, 2001) with comparative tests in English, Spanish, Swedish and Basque lexical-sample sense tagging over a combined sample of 37730 instances of 234 polysemous words.</Paragraph>
    <Paragraph position="2"> This paper offers a detailed comparative evaluation and description of the problem of classifier combination over a structurally and procedurally diverse set of six both well established and original classifiers: extended Naive Bayes, BayesRatio, Cosine, non-hierarchical Decision Lists, Transformation Based Learning (TBL), and the MMVC classifiers, briefly described in Section 4. These systems have different space-searching strategies, ranging from discriminant functions (BayesRatio) to data likelihood (Bayes, Cosine) to decision rules (TBL, Decision Lists), and therefore are amenable to combination.</Paragraph>
  </Section>
class="xml-element"></Paper>