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<Paper uid="P99-1069">
  <Title>Estimators for Stochastic &amp;quot;Unification-Based&amp;quot; Grammars*</Title>
  <Section position="7" start_page="539" end_page="539" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper described a log-linear model for SUBGs and evaluated two estimators for such models. Because estimators that can estimate rule features for SUBGs can also estimate other kinds of features, there is no particular reason to limit attention to rule features in a SUBG. Indeed, the number and choice of features strongly influences the performance of the model. The estimated models are able to identify the correct parse from the set of all possible parses approximately 50% of the time.</Paragraph>
    <Paragraph position="1"> We would have liked to introduce features corresponding to dependencies between lexical items. Log-linear models are well-suited for lexical dependencies, but because of the large number of such dependencies substantially larger corpora will probably be needed to estimate such models. 1 1Alternatively, it may be possible to use a simpler non-SUBG model of lexical dependencies estimated from a much larger corpus as the reference distribution with  parses of the test corpus that were the correct parses, and -log PL(wtest) is the negative logarithm of the pseudo-likelihood of the test corpus.</Paragraph>
    <Paragraph position="2"> However, there may be applications which can benefit from a model that performs even at this level. For example, in a machine-assisted translation system a model like ours could be used to order possible translations so that more likely alternatives are presented before less likely ones. In the ambiguity-preserving translation framework, a model like this one could be used to choose between sets of analyses whose ambiguities cannot be preserved in translation.</Paragraph>
  </Section>
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