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<Paper uid="C04-1131">
  <Title>Word sense disambiguation criteria: a systematic study</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The task of word sense disambiguation (WSD) is to identify the correct sense of a word in context.</Paragraph>
    <Paragraph position="1"> WSD is usually performed by matching information from the context in which the word occurs with disambiguation knowledge source. Our approach uses supervised machine-learning techniques to automatically acquire such disambiguation knowledge from sense-tagged corpora. At present, this type of approach is widely used and seems to yield the best results (Kilgarriff, Rosenzweig, 2000; Ng, 1997b).</Paragraph>
    <Paragraph position="2"> Information conveyed by context words (morphological form) is enriched with further annotations: part-of-speech tag, lemma, etc. Each individual piece of information is called a feature. A good feature should capture an important source of knowledge that is critical in determining the sense of the word to be disambiguated. The choice of the appropriate set of features is an important issue for WSD (Bruce, Wiebe, Perdersen, 1996; Ng, Zelle, 1997; Pedersen, 2001). Thus, this paper describes the results of a systematic and in-depth study of homogenous criteria (i.e. set of features) that can be used for WSD.</Paragraph>
  </Section>
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