This operation shrinks or prunes an n-gram language model in one of three ways:

  • count pruning: prunes based on count cutoffs for the various n-gram orders specified by count_pattern.
  • relative entropy: prunes based on a relative entropy criterion theta.
  • Seymore: prunes based on the Seymore-Rosenfeld criterion theta.

The C++ classes are all derived from the base class NGramShrink.


ngramshink [--opts] [in.mod [out.mod]]
  --method: type = string, one of: count_prune (default) | relative_entropy | seymore
  --count_pattern: type = string, default = ""
  --theta, type = double, default = 0.0
 class NGramCountPrune(StdMutableFst *model, string count_pattern);
 class NGramRelativeEntropy(StdMutableFst *model, double theta);
 class NGramSeymoreShrink(StdMutableFst *model, double theta);


ngramshrink --method=relative_entropy --theta=1.0e-7 in.mod >out.mod

StdMutableFst *model = StdMutableFst::Read("in.mod", true);
NGramRelativeEntropy ngram(model, 1.0e-7);


For relative entropy or Seymore shrinking, the input n-gram model must be weight-normalized (the probabilities at each state must sum to 1). For count pruning, either a normalized model or raw, unnormalized counts can be used.


K. Seymore and R. Rosenfeld. "Scalable Backoff Language Models", Proc. of International Conference on Speech and Language Processing. 1996.

A. Stolcke. "Entropy-based Pruning of Backoff Language Models", Proc. of DARPA Broadcast News Transcription and Understanding Workshop. 1998.

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Topic revision: r7 - 2012-03-08 - MichaelRiley
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