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<Paper uid="W95-0103">
  <Title>Prepositional Phrase Attachment through a Backed-Off Model</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Recent work has considered corpus-based or statistical approaches to the problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb phrases of the form v rip1 p rip2 are resolved through a model which considers values of the four head words (v, nl, p and 77,2). This paper shows that the problem is analogous to n-gram language models in speech recognition, and that one of the most common methods for language modeling, the backed-off estimate, is applicable. Results on Wall Street Journal data of 84.5% accuracy are obtained using this method.</Paragraph>
    <Paragraph position="1"> A surprising result is the importance of low-count events - ignoring events which occur less than 5 times in training data reduces performance to 81.6%.</Paragraph>
  </Section>
class="xml-element"></Paper>