Keywords
- Language Model
- Smoothing Method
- Training Corpus
- Word Sequence
- Escape Probability
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Placeway, P., Schwartz R., Fung P., and Nguyen, L., 1993, The Estimation of Powerful Language Models from Small and Large corpora, ICASSP-93, Vol. 2, pp. 33-36.
Brown P. F., Della Pietra S. A., Della Pietra V. J., Lai J. C., and Mercer R. L., 1992, An Estimate of an Upper Bound for the Entropy of English, Computational Linguistics, Vol. 18, pp. 31-40.
Chen Standy F. and Goodman Joshua, 1999, An Empirical study of smoothing Techniques for Language Modeling, Computer Speech and Language, Vol. 13,pp. 359-394.
Church K. W. and Gale W. A., 1991, A Comparison of the Enhanced Good-Turing and Deleted Estimation Methods for Estimating Probabilies of English Bigrams, Computer Speech and Language, Vol. 5, pp 19-54.
Dagan I., Maucus S and Markovitch, 1995, Contextual Word Similarity and Estimation from Sparse Data, Computer Speech and Language, Vol. 9, pp. 123-152.
Essen U. and Steinbiss, 1992, Cooccurrence Smoothing for Stochastic Language Modelling, IEEE International conference on Acoustic, Speech and Signal Processing, Vol. 1, pp. 161-164.
Good I. J., 1953, The Population Frequencies of Species and the Estimation of Population Parameters, Biometrika, Vol. 40, pp. 237-264.
Jelinek F., 1997, Automatic Speech Recognition-Statistical Methods, M.I.T.
Jelinek F. and Mercer R. L., 1980, Interpolated Estimation of Markov Source Parameters from Spars Data, Proceedings of the Workshop on Pattern Recognition in Practice, North-Holland, Amsterdam, The Northlands, pp. 381-397.
Juraskey D. and Martin James H., 2000, Speech and Language Processing, Prentice Hall.
Katz S. M., March 1987, Estimation of Probabilities from Sparse Data for the Language Models Component of a Speech Recognizer, IEEE Trans. On Acoustic, Speech and Signal Processing, Vol. ASSP-35, pp. 400-401.
Knerser R. and Ney H., 1995, Improved Backing-Off for M-gram Language Modeling, IEEE International conference on Acoustic, Speech and Signal Processing, pp. 181-184.
Nádas A., 1984, Estimation of Probabilities in the Language Model of the IBM Speech Recognition System, IEEE Transactions on Acoustics, Speech, Signals Processing, Vol. 32, No. 4, pp. 859-861.
Nádas A., 1985, On Turing’s Formula for Word Probabilities, IEEE Trans. On Acoustic, Speech and Signal Processing, Vol. ASSP-33, pp. 1414-1416.
Ney H. and Essen U., 1991, On Smoothing Techniques for Bigram-Based Natural Language Modeling, IEEE International conference on Acoustic, Speech and Signal Processing, pp. 825-828.
Su K. Y., Chiang T. H., Chang J. S., A Overview of Corpus-Based Statistical-Oriented (CBSO) Techniques for Natural Language Processing, Computational Linguistics and Chinese Language Processing, vol. 1, no. 1, pp.101-157, August 1996.
Witten L. H. and Bell T. C., 1991, The Zero-Frequency Problem: Estimating the Probabilities of Novel Events in Adaptive Text Compression, IEEE Transaction on Information theory, Vol. 37, No. 4, pp. 1085-1094.
Huang C.-R., 1995, Introduction to the Academic Sinica Balance Corpus, Proceeding of ROCLLING VII, pp. 81-99.
Algort P. H. and Cover T. M., 1988, A Sandwich Proof of the Shannon- McMillan-Breiman Theorem, Ahe Annals of Probability, Vol. 16, No. 2, pp. 899-909.
Jurafsky D. and Martin J. H., 2000, Speech and Language Processing, Prentice Hall, Chapter 6.
Juang B. H and Lo S. H., 1994, On the Bias if the Turing-Good Estimate of Probabilities, IEEE Trans. On Signal Processing, Vol. 42, No. 2, pp. 496-498.
Hermann Ney, Ute Essen, Reinhard Kneser, December 1995, On the Estimation of ’Small’ Probabilities by Leaving-One-Out, Vol.17, No. 12, IEEE PAMI, pp. 1202-1212.
Xuehua Shen, ChengXiang Zhai, Active Feedback in Ad Hoc Information Retrieval, Proceedings of ACM SIGIR 2005.
Seung-Hoon Na, In-Su Kang, Ji-Eun Roh, Jong-Hyeok Lee, An Empirical Study of Query Expansion and Cluster-Based Retrieval in Language Modeling Approach, AIRS 2005.
Guihong Cao, Jian-Yun Nie, Jing Bai, Integrating Word Relationships into Language Models Proceedings of ACM SIGIR 2005
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Huang, FL., Yu, MS. (2007). Analyzing the Statistical Behavior of Smoothing Method. In: Sobh, T. (eds) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6268-1_35
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DOI: https://doi.org/10.1007/978-1-4020-6268-1_35
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