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<Paper uid="W95-0103">
  <Title>Prepositional Phrase Attachment through a Backed-Off Model</Title>
  <Section position="8" start_page="36" end_page="36" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> The backed-off estimate scores appreciably better than other methods which have been tested on the Wall Street Journal corpus. The accuracy of 84.5% is close to the hmna.n peribrnlance figure of 88% using the 4 head words alone. A particularly surprising result is the significance of low count events in training data. The Mgorithm has the additional advantages of being conceptually simple, and computationMly inexpensive to implement.</Paragraph>
    <Paragraph position="1"> There are a few possible improvements which may raise performance further. Firstly, while we have shown the importance of low-count events, some kind of smoothing 1nay improve peribrmance further - this needs to be investigated. Word-classes of semantically similar words may be used to help the sparse data problem - both \[RRR94\] and \[BR94\] report significant improvements through the use of word-classes. Finally, more training data is almost certain to improve results.</Paragraph>
  </Section>
class="xml-element"></Paper>