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Prepositional Phrase Attachment Through a Backed-off Model

  • M. Collins
  • J. Brooks
Part of the Text, Speech and Language Technology book series (TLTB, volume 11)

Abstract

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 np1 p np2 are resolved through a model which considers values of the four head words (v, n1, p and n2). 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. 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%.

Keywords

Wall Street Journal Head Noun Maximum Entropy Model Sparse Data Problem Head Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • M. Collins
  • J. Brooks

There are no affiliations available

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