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<Paper uid="W00-0105">
  <Title>Dependency of context-based Word Sense Disambiguation from representation and domain complexity</Title>
  <Section position="4" start_page="32" end_page="32" type="concl">
    <SectionTitle>
4. Conclusion
</SectionTitle>
    <Paragraph position="0"> By no means the work presented in this paper needs more investigation, especially on the experimental side. However, we believe that learnability analysis of WSD models has strong practical implications.</Paragraph>
    <Paragraph position="1"> The quantitative and (preliminary) experimental results of Section 2 put in evidence that : * In order to acquire statistically stable contextual models of linguistic concepts, the dimension of the analyzed corpora must be considerably high. Paradoxically, untrained probabilistic systems are in better shape in this regard. Very large repositories of language samples can be now obtained from the WWW.</Paragraph>
    <Paragraph position="2"> * The experimental setting (i.e. size of the training set) must be tuned for each category and language domain, because the variability of contextual behavior may be significantly different, depending on domain complexity, e.g. the type and grain of the selected category, and the more or less restricted language domain * it is possible and indeed advisable, for a given WSD algorithm, to determine in a formal way the relation between expected accuracy of the WSD model and the domain and representation complexity.</Paragraph>
    <Paragraph position="3"> This would allow a better comparison among systems, and an a-priori tuning of the parameters of the disambiguation model.</Paragraph>
  </Section>
class="xml-element"></Paper>