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Beyond Word N-Grams

  • F. C. Pereira
  • Y. Singer
  • N. Tishby
Part of the Text, Speech and Language Technology book series (TLTB, volume 11)

Abstract

We describe, analyze, and evaluate experimentally a new probabilistic model for word-sequence prediction in natural language based on prediction suffix trees (PSTs). By using efficient data structures, we extend the notion of PST to unbounded vocabularies. We also show how to use a Bayesian approach based on recursive priors over all possible PSTs to efficiently maintain tree mixtures. These mixtures have provably and practically better performance than almost any single model. We evaluate the model on several corpora. The low perplexity achieved by relatively small PST mixture models suggests that they may be an advantageous alternative, both theoretically and practically, to the widely used n-gram models.

Keywords

Online Mode Distinct Word Paradise Lost Brown Corpus Splay Tree 
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

  • F. C. Pereira
  • Y. Singer
  • N. Tishby

There are no affiliations available

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