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<Paper uid="C00-2094">
  <Title>Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution</Title>
  <Section position="7" start_page="654" end_page="654" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> The disanfl3iguation method presented in this pa.per delibera.tely is restricted to the limited mnomlt of information provided by a probabilistic class-based lexicon. This intbrmation yet proves itself accurate enough to yield good empirical results, e.g., in target-language disambiguation. The t)rol)al)ilistic class-based lexica are induced in an unsupervised manner fl'om large mmnnotated corpora. Once the lexica are constructed, lexical mnbiguity resolution can be done by a simple lexicon look-up. I51 target-word selection, the nlOSt fl'equent target-noun whose semantics fits best to tit(; semantics of the argument-slot of the target-verb is chosen. We evaluated our method on randomly selected examities Dora real-world bilingual corpora which constitutes a realistic hard task. Dismnbiguation based on probabilistie lexica perfornmd satisfim-' tory for this |;ask. The lesson lem'ned tYom our experimental results is that hybrid models con&gt; bining fi:equency information and class-based t)robabilities outlmrtbnn both pure fl'equency-based models and pure clustering models. 1'511&amp;quot;ther improvements are to be expected from extended lexica including, e.g., adjectival and prepositional predicates.</Paragraph>
  </Section>
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