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<Paper uid="W00-0706">
  <Title>A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation*</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
Word Sense Disambiguation (WSD) is the prob-
</SectionTitle>
    <Paragraph position="0"> lem of assigning the appropriate meaning (or sense) to a given word in a text or discourse.</Paragraph>
    <Paragraph position="1"> Resolving the ambiguity of words is a central problem for large scale language understanding applications and their associate tasks (Ide and V4ronis, 1998). Besides, WSD is one of the most important open problems in NLP. Despite the wide range of approaches investigated (Kilgarrift and Rosenzweig, 2000) and the large effort devoted to tackling this problem, to date, no large-scale broad-coverage and highly accurate WSD system has been built.</Paragraph>
    <Paragraph position="2"> One of the most successful current lines of research is the corpus-based approach, in which statistical or Machine Learning (M L) algorithms have been applied to learn statistical models or classifiers from corpora in order to per* This research has been partially funded by the Spanish Research Department (CICYT's project TIC98-0423-C06), by the EU Commission (NAMIC I8T-1999-12392), and by the Catalan Research Department (CIRIT's consolidated research group 1999SGR-150 and CIRIT's grant 1999FI 00773).</Paragraph>
    <Paragraph position="3"> form WSD. Generally, supervised approaches (those that learn from previously semantically annotated corpora) have obtained better results than unsupervised methods on small sets of selected ambiguous words, or artificial pseudowords. Many standard M L algorithms for supervised learning have been applied, such as: Decision Lists (Yarowsky, 1994; Agirre and Martinez, 2000), Neural Networks (Towell and Voorhees, 1998), Bayesian learning (Bruce and Wiebe, 1999), Exemplar-based learning (Ng, 1997), Boosting (Escudero et al., 2000a), etc.</Paragraph>
    <Paragraph position="4"> Further, in (Mooney, 1996) some of the previous methods are compared jointly with Decision Trees and Rule Induction algorithms, on a very restricted domain.</Paragraph>
    <Paragraph position="5"> Although some published works include the comparison between some alternative algorithms (Mooney, 1996; Ng, 1997; Escudero et al., 2000a; Escudero et al., 2000b), none of them addresses the issue of the portability of supervised ML algorithms for WSD, i.e., testing whether the accuracy of a system trained on a certain corpus can be extrapolated to other corpora or not. We think that the study of the domain dependence of WSD --in the style of other studies devoted to parsing (Sekine, 1997; Ratnaparkhi, 1999)-- is needed to assure the validity of the supervised approach, and to determine to which extent a tuning pre-process is necessary to make real WSD systems portable.</Paragraph>
    <Paragraph position="6"> In this direction, this work compares five different M L algorithms and explores their portability and tuning ability by training and testing them on different corpora.</Paragraph>
  </Section>
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