The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A maximum entropy approach to adaptive statistical language modelling
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Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Word association norms, mutual information, and lexicography
Church, K., & Hanks, P. (1990) · doi:10.5555/89086.89095 · Cited by 3,590
THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS
GOOD, I. J. (1953) · Biometrika · doi:10.1093/biomet/40.3-4.237 · Cited by 3,437
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
Prediction and Entropy of Printed English
Shannon, C. E. (1951) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1951.tb01366.x · Cited by 2,686
Estimation of probabilities from sparse data for the language model component of a speech recognizer
Katz, S. (1987) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1987.1165125 · Cited by 1,637
Improved backing-off for M-gram language modeling
Kneser, R., & Ney, H. (2002) · 1995 International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1995.479394 · Cited by 1,473
A Maximum Likelihood Approach to Continuous Speech Recognition
Bahl, L. R., Jelinek, F., & Mercer, R. L. (1983) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1983.4767370 · Cited by 1,212
Cited by
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 960
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821
Continuous space language models
Schwenk, H. (2007) · Computer Speech & Language · doi:10.1016/j.csl.2006.09.003 · Cited by 516
Domain Adaptation: Learning Bounds and Algorithms
Mansour, Y., Mohri, M., & Rostamizadeh, A. (2009) · arXiv · doi:10.48550/arxiv.0902.3430 · Cited by 504
Metadata
- Authors
- Rosenfeld, R.
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 1996-07
- Volume
- 10
- Issue
- 3
- Pages
- 187-228
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W1996903695
- Cited by
- 542
- References
- 32
- FWCI
- 15.38
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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How to cite
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- CoChat. (2026). Citation check: A maximum entropy approach to adaptive statistical language modelling. https://app.cochat.ai/papers/10.1006/csla.1996.0011
- The paper
- Rosenfeld, R. (1996). A maximum entropy approach to adaptive statistical language modelling. Computer Speech & Language. https://doi.org/10.1006/csla.1996.0011
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
