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<Paper uid="N06-2039">
  <Title>Unsupervised Induction of Modern Standard Arabic Verb Classes</Title>
  <Section position="9" start_page="155" end_page="155" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We successfully perform the novel task of applying clustering techniques to verb frame information acquired from the ATB to induce lexical semantic classes for MSA verbs. In doing this, we find that the quality of the clusters is sensitive to the inclusion of information about lexical heads of the constituents in the syntactic frames, as well as parameters of the clustering algorithm. Our classification performs well with respect to a gold standard clusters produced by noisy translations of English verbs in the Levin classes. Our best clustering condition when we use all frame information and the most frequent verbs in the ATB and a high number of clusters outperforms a random baseline by F b=1 difference of 0.13. This analysis leads us to conclude that the clusters are induced from the structure in the data Our results are reported with a caveat on the gold standard data. We are in the process of manually cleaning the English translations corresponding to the MSA verbs.</Paragraph>
  </Section>
class="xml-element"></Paper>