The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Modeling for text compression
Open accessAbstract
The best schemes for text compression use large models to help them predict which characters will come next. The actual next characters are coded with respect to the prediction, resulting in compression of information. Models are best formed adaptively, based on the text seen so far. This paper surveys successful strategies for adaptive modeling that are suitable for use in practical text compression systems. The strategies fall into three main classes: finite-context modeling, in which the last few characters are used to condition the probability distribution for the next one; finite-state modeling, in which the distribution is conditioned by the current state (and which subsumes finite-context modeling as an important special case); and dictionary modeling, in which strings of characters are replaced by pointers into an evolving dictionary. A comparison of different methods on the same sample texts is included, along with an analysis of future research directions.
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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
The Art of Computer Programming
Knuth, D. E. (1968) · Cited by 16,000
A Method for the Construction of Minimum-Redundancy Codes
Huffman, D. (1952) · Proceedings of the IRE · doi:10.1109/jrproc.1952.273898 · Cited by 6,334
A universal algorithm for sequential data compression
Ziv, J., & Lempel, A. (1977) · IEEE Transactions on Information Theory · doi:10.1109/tit.1977.1055714 · Cited by 5,456
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697196 · Cited by 4,297
The Art of Computer Programming: Volume 3: Sorting and Searching
Knuth, D. E. (1973) · Cited by 3,955
Compression of individual sequences via variable-rate coding
Ziv, J., & Lempel, A. (1978) · IEEE Transactions on Information Theory · doi:10.1109/tit.1978.1055934 · Cited by 3,432
Arithmetic coding for data compression
Witten, I. H., Neal, R. M., & Cleary, J. G. (1987) · Communications of the ACM · doi:10.1145/214762.214771 · Cited by 2,877
Self-citationOn the Complexity of Finite Sequences
Lempel, A., & Ziv, J. (1976) · IEEE Transactions on Information Theory · doi:10.1109/tit.1976.1055501 · Cited by 2,717
Metadata
- Authors
- Bell, T., Witten, I. H., & Cleary, J. G.
- Venue
- ACM Computing Surveys
- Published
- 1989-12
- Volume
- 21
- Issue
- 4
- Pages
- 557-591
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2012603689
- Cited by
- 261
- References
- 53
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Bell, T., Witten, I. H., & Cleary, J. G. (1989). Modeling for text compression. ACM Computing Surveys. https://doi.org/10.1145/76894.76896
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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.
