Paper Title:
IMPROVING LANGUAGE MODEL SIZE REDUCTION USING BETTER PRUNING CRITERIA

Authors: Jianfeng Gao and Min Zhang

Other assigned terms:

  • alphabet
  • ambiguity
  • backoff
  • backoff n-gram model
  • bigram
  • bigram model
  • character error rate
  • characters
  • chinese characters
  • chinese text
  • coefficient
  • comparative study
  • conditional probabilities
  • conditional probability
  • corpora
  • correlation
  • correlation coefficient
  • data sparseness
  • distribution
  • document
  • entropy
  • error rate
  • estimation
  • evaluation measure
  • evaluation measures
  • experimental results
  • fact
  • finite set
  • hypothesis
  • kullback-leibler divergence
  • language model
  • language model probability
  • language models
  • lexicon
  • likelihood
  • linear regression model
  • linguistics
  • measure
  • measures
  • method
  • model perplexity
  • model probability
  • model size
  • n-gram
  • n-gram model
  • n-gram models
  • normalization factor
  • ordered list
  • perplexity
  • phonetic alphabet
  • pinyin
  • probabilities
  • probability
  • probability distribution
  • probability estimate
  • pruning bigram
  • pruning threshold
  • rank-order correlation coefficient
  • regression model
  • search space
  • style
  • target word
  • terms
  • test corpora
  • test data
  • test set
  • text
  • training
  • training data
  • transcript
  • trigram
  • unigram
  • vocabulary
  • vocabulary size
  • word
  • word error rate
  • word pair
  • word string
  • word strings
  • words

Extracted Section Types:


This page last edited on 06 October 2025.




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