Adaptive language modeling using minimum discriminant estimation

@article{Pietra1992AdaptiveLM,
  title={Adaptive language modeling using minimum discriminant estimation},
  author={Stephen Della Pietra and Vincent J. Della Pietra and Robert L. Mercer and Salim Roukos},
  journal={[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing},
  year={1992},
  volume={1},
  pages={633-636 vol.1}
}
The authors present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, they find the closest n-gram distribution to the static n-gram distribution (using the discrimination information distance measure) that satisfies the marginal constraints derived from the document. The resulting minimum discrimination information model results in a perplexity of 208… CONTINUE READING

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