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<Paper uid="J03-1004">
  <Title>Bies, Ann, Mark Ferguson, Karen Katz, and</Title>
  <Section position="5" start_page="94" end_page="94" type="concl">
    <SectionTitle>
6. Conclusion
</SectionTitle>
    <Paragraph position="0"> In this article, we have taken a statistical, corpus-based approach to the modeling of quantifier scope preferences, a subject that has previously been addressed only with systems of ad hoc rules derived from linguists' intuitive judgments. Our model takes its theoretical inspiration from Kuno, Takami, and Wu (1999), who suggest an &amp;quot;expert system&amp;quot; approach to scope preferences, and follows many other projects in the machine learning of natural language that combine information from multiple sources in solving linguistic problems.</Paragraph>
    <Paragraph position="1"> 0ur results are generally supportive of the design that Kuno, Takami, and Wu propose for the quantifier scope component of grammar, and some of the features induced by our models find clear parallels in the factors that Kuno, Takami, and Wu claim to be relevant to scoping. In addition, our final experiment, in which we combine our quantifier scope module with a PCFG model of syntactic phrase structure, provides evidence of a grammatical architecture in which different aspects of structure mutually constrain one another. This result casts doubt on approaches in which syntactic processing is completed prior to the determination of other grammatical properties of a sentence, such as quantifier scope relations.</Paragraph>
    <Paragraph position="2"> Appendix: Selected Codes Used to Annotate Syntactic Categories in the Penn Treebank, from Marcus et al. (1993) and Bies et al. (1995)</Paragraph>
  </Section>
class="xml-element"></Paper>