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<Paper uid="C00-1029">
  <Title>A Class-based Probabilistic approach to Structural Disambiguation</Title>
  <Section position="7" start_page="199" end_page="199" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have shown that when instances of Word-Net are well populated with examples of n2, the method described here for solving P1)-attachment ambiguities is highly accurate.</Paragraph>
    <Paragraph position="1"> When WordNet is sparsely populated, the method automatically resorts to comparing just the preposition and each of the potential attachment sites, as the similarity-class technique will select {root} as the appropriate level of general\]sat\]on for n2 in such cases. We have also shown the similarity-class technique to be superior to using a fixed level of general\]sat\]on in WordNet.</Paragraph>
    <Paragraph position="2"> Further work will look at how to integrate probabilities such as p(clv, r) into a model of dependency structure, similar to that of Collins (1996) and Collins (1997), which can be used \['or parse selection. However, knowledge of se\]ectional preferences cannot by itself solve the problem of structural disambiguation, and this further work will also look at using additional knowledge, such a.s subcategorisation information. null</Paragraph>
  </Section>
class="xml-element"></Paper>