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A comparison of algorithms for maximum entropy parameter estimation
Open accessAbstract
Conditional maximum entropy (ME) models provide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety of classification problems in natural language processing. However, the flexibility of ME models is not without cost. While parameter estimation for ME models is conceptually straightforward, in practice ME models for typical natural language tasks are very large, and may well contain many thousands of free parameters. In this paper, we consider a number of algorithms for estimating the parameters of ME models, including iterative scaling, gradient ascent, conjugate gradient, and variable metric methods. Sur-prisingly, the standardly used iterative scaling algorithms perform quite poorly in comparison to the others, and for all of the test problems, a limited-memory variable metric algorithm outperformed the other choices.
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Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Deming, W. E., & Stephan, F. F. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731829 · Cited by 1,537
Cited by
Shallow parsing with conditional random fields
Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246
McCallum, A., & Li, W. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119206 · Cited by 1,160
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
Charniak, E., & Johnson, M. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219862 · Cited by 872
Domain Adaptation for Statistical Classifiers
Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816
Discriminative Reranking for Natural Language Parsing
Collins, M., & Koo, T. (2005) · Computational Linguistics · doi:10.1162/0891201053630273 · Cited by 710
Metadata
- Authors
- Malouf, R.
- Venue
- proceeding of the 6th conference on Natural language learning - COLING-02
- Published
- 2002
- Volume
- 20
- Pages
- 1-7
- OpenAlex
- W2102667697
- Cited by
- 655
- References
- 2
- FWCI
- 28.27
- Type
- conference paper
- Language
- en
- Open access
- gold
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