Adaptive Statistical Language Modeling; A Maximum Entropy Approach

@inproceedings{Rosenfeld1994AdaptiveSL,
  title={Adaptive Statistical Language Modeling; A Maximum Entropy Approach},
  author={Ronald Rosenfeld},
  year={1994}
}
Language modeling is the attempt to characterize, capture and exploit regularities in natural language. In statistical language modeling, large amounts of text are used to automatically determine the model's parameters. Language modeling is useful in automatic speech recognition, machine translation, and any other application that processes natural language with incomplete knowledge. In this thesis, I view language as an information source which emits a stream of symbols from a finite alphabet… CONTINUE READING

Citations

Publications citing this paper.

720 Citations

02040'93'98'04'10'16
Citations per Year
Semantic Scholar estimates that this publication has 720 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 17 REFERENCES

published by Morgan Kaufmann

  • Ronald Rosenfeld, Xuedong Huang. Improvements in Stochastic Language Modelin Speech, Natural Language
  • pages 107-111, February
  • 1992
Highly Influential
2 Excerpts

Oral presentation at ARPA Spoken Language Systems Workshop

  • Ronald Rosenfeld. Language Model Adaptation in ARPA's CSR Evaluation
  • March
  • 1994

pages 242-242e

  • A. Ratnaparkhi, S. Roukos. A Maximum Entropy Model for Prepositional Technology
  • March
  • 1994

Mercer

  • L Robert
  • Personal communication.
  • 1992
1 Excerpt

Mercer and Salim Roukos

  • L Robert
  • Personal communication.
  • 1992

Germany

  • Ute Essen, Hermann Ney. Statistical Language Modelling Using a Cache Conference, University of Trier
  • September
  • 1991

pages 293-295

  • F. Jelinek, B. Merialdo, S. Roukos, M. Strauss. A Dynamic Language Model for Speech Recog Speech, Natural Language
  • February
  • 1991
1 Excerpt

Similar Papers

Loading similar papers…