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<Paper uid="W02-0903">
  <Title>Boosting automatic lexical acquisition with morphological informationa0</Title>
  <Section position="10" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> All the approaches cited above focus on some aspect of the problem of lexical acquisition. What we learn from them is that information about the meaning of words comes in very different forms. One thing that needs to be investigated is the design of better sets of features that encode the information that has been found useful in these studies. For example, it is known from work in word sense disambiguation that conditioning on distance and syntactic relations can be very helpful. For a model for lexical acquisition to be successful it must be able to combine as many sources of information as possible. We found that boosting is a viable method in this respect. In particular, in this paper we showed that morphology is one very useful source of information, independent of frequency, that can be easily encoded in simple features.</Paragraph>
    <Paragraph position="1"> A more general finding was that inserting new words into a dictionary is a hard task. For these classifiers to become useful in practice, much better accuracy is needed. This raises the question of the scalability of machine learning methods to multiclass classification for very large lexicons. Our impression on this is that directly attempting classification on tens of thousands of classes is not a viable approach. However, there is a great deal of information in the structure of a lexicon like Wordnet. Our guess is that the ability to make use of structural information will be key in successful approaches to this problem.</Paragraph>
  </Section>
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