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<Paper uid="C92-2066">
  <Title>Stochastic Lexicalized Tree-Adjoining Grammars *</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The notion of stochastic lexicalized tree-adjoining grammar (SLTAG) is formally defined. The parameters of a SLTAG correspond to the probability of combining two structures each one associated with a word. The characteristics of SLTAG are unique and novel since it is lexieally sensitive (as N-gram models or Hidden Markov Models) and yet hierarchical (as stochastic context-free grammars).</Paragraph>
    <Paragraph position="1"> Then, two basic algorithms for SLTAG arc introduced: an algorithm for computing the probability of a sentence generated by a SLTAG and an inside-outsidelike iterative algorithm for estimating the parameters of a SLTAG given a training corpus.</Paragraph>
    <Paragraph position="2"> Finally, we should how SLTAG enables to define a lexicalized version of stochastic context-free grammars and we report preliminary experiments showing some of the advantages of SLTAG over stochastic context-free grammars.</Paragraph>
  </Section>
class="xml-element"></Paper>