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<Paper uid="P06-2029">
  <Title>The Benefit of Stochastic PP Attachment to a Rule-Based Parser</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> To study PP attachment disambiguation as a benchmark for empirical methods in natural language processing it has often been reduced to a binary decision problem (between verb or noun attachment) in a particular syntactic configuration. A parser, however, must solve the more general task of deciding between more than two alternatives in many different contexts. We combine the attachment predictions made by a simple model of lexical attraction with a full-fledged parser of German to determine the actual benefit of the subtask to parsing. We show that the combination of data-driven and rule-based components can reduce the number of all parsing errors by 14% and raise the attachment accuracy for dependency parsing of German to an unprecedented 92%.</Paragraph>
  </Section>
class="xml-element"></Paper>