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<Paper uid="W04-0839">
  <Title>Complementarity of Lexical and Simple Syntactic Features: The SyntaLex Approach to SENSEVAL-3</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The SyntaLex systems are supervised learners that identify the intended sense of a word (target word) given its context. They are derived from the Duluth systems that participated in SENSEVAL-2, and which are more fully described in (Pedersen, 2001b).</Paragraph>
    <Paragraph position="1"> The context of a word is a rich source of discrete features which lend themselves nicely to decision tree learning. Prior research (e.g., (McRoy, 1992), (Ng and Lee, 1996), (Stevenson and Wilks, 2001), (Yarowsky and Florian, 2002)) suggests that use of both syntactic and lexical features will improve disambiguation accuracies. There has also been considerable work on word sense disambiguation using various supervised learning algorithms. However, both (Pedersen, 2001a) and (Lee and Ng, 2002) show that different learning algorithms produce similar results and that the use of appropriate features may dramatically improve results. Thus, our focus is not on the learning algorithm but on the features used and their dynamics.</Paragraph>
    <Paragraph position="2"> Our systems use bigrams and Part of Speech features individually, in a simple ensemble and as part of single classifier using both kinds of features. We also show that state of the art results (72.1%, coarse grained accuracy) can be achieved using just these simple sets of features.</Paragraph>
  </Section>
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