Similarity-Based Models of Word Cooccurrence Probabilities

@article{Dagan1999SimilarityBasedMO,
  title={Similarity-Based Models of Word Cooccurrence Probabilities},
  author={Ido Dagan and Lillian Lee and Fernando Pereira},
  journal={Machine Learning},
  year={1999},
  volume={34},
  pages={43-69}
}
In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach” and ”eat a beach” is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given… CONTINUE READING
Highly Influential
This paper has highly influenced 30 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 409 citations. REVIEW CITATIONS
244 Citations
51 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 244 extracted citations

410 Citations

02040'98'02'07'12'17
Citations per Year
Semantic Scholar estimates that this publication has 410 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 51 references

Co-occurrence smoothing for stochastic language modeling

  • U. Essen, V. Steinbiss
  • In ICASSP 92
  • 1992
Highly Influential
8 Excerpts

Diversity: Its measurement, decomposition, apportionment and analysis

  • C. R. Rao
  • Sankyhā: The Indian Journal of Statistics, 44 (A…
  • 1982
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…