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Cited by 564

The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.

An Introduction to Conditional Random Fields

Open access
Sutton, Charles, McCallum, Andrew·2012·Foundations and Trends® in Machine Learning
DOI 10.1561/2200000013
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Abstract

Many tasks involve predicting a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling. They combine the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This survey describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in many areas, including natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large-scale CRFs. We do not assume previous knowledge of graphical modeling, so this survey is intended to be useful to practitioners in a wide variety of fields.

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Cites

Research works printed but not linked: 40

  1. Blunsom, 2006, Discriminative word alignment with conditional random fields, 65
  2. Bradley, 2010, Learning tree conditional random fields
  3. Clark, 2004, Parsing the WSJ using CCG and log-linear models, 103
  4. Cohn, 2006, European Conference on Machine Learning (ECML), 606
  5. Cowans, 2005, A graphical model for simultaneous partitioning and labeling
  6. Crammer, 2003, Ultraconservative online algorithms for multiclass problems, Journal of Machine Learning Research, 3, 951
  7. Culotta, 2004, First Conference on Email and Anti-Spam (CEAS)
  8. Culotta, 2004, Confidence estimation for information extraction, 10.3115/1613984.1614012 · doi:10.3115/1613984.1614012
  9. Daumé III, 2005, International Conference on Machine Learning (ICML)
  10. Dillon, 2010, Stochastic composite likelihood, Journal of Machine Learning Research, 11, 2597
  11. Globerson, 2007, Exponentiated gradient algorithms for log-linear structured prediction, 10.1145/1273496.1273535 · doi:10.1145/1273496.1273535
  12. Goodman, 2004, Exponential priors for maximum entropy models
  13. Graca, 2009, Posterior vs parameter sparsity in latent variable models, 664
  14. Kim, 2004, International joint workshop on natural language processing in biomedicine and its applications, 70
  15. Kulesza, 2008, Structured learning with approximate inference
  16. Le Cun, 1998, Neural Networks, Tricks of the Trade, Lecture Notes in Computer Scienc
  17. LeCun, 2007, Predicting Structured Data
  18. McCallum, 2000, International Conference on Machine Learning (ICML), 591
  19. McCallum, 2005, Advances in Neural Information Processing Systems 17, 905
  20. Miller, 2- 7 2004, HLT-NAACL 200f: Main Proceedings, 337
  21. Murray, 2006, Uncertainty in Artificial Intelligence (UAI), 359
  22. Ng, 2004, Feature selection, 11 vs. 12 regularization, and rotational invariance
  23. Ng, 2002, Advances in Neural Information Processing Systems 14, 841
  24. Qi, 2005, Bayesian conditional random fields
  25. Qi, 2005, Diagram structure recognition by Bayesian conditional random fields
  26. Rosenberg, 2007, Mixture-of-parents maximum entropy Markov models
  27. Sang, 2003, Proceedings of CoNLL-2003, 142
  28. Sarawagi, 2005, Advances in Neural Information Processing Systems 17, 1185
  29. Sato, 2005, RNA secondary structural alignment with conditional random fields, Bioinformatics, 21, Ü237, 10.1093/bioinformatics/bti1139 · doi:10.1093/bioinformatics/bti1139
  30. Singla, 2005, Proceedings of the National Conference on Artificial Intelligence, 868
  31. Stern, 2005, Advances in Neural Information Processing Systems 17, 1353
  32. Sutton, 2007, Introduction to Statistical Relational Learning
  33. Sutton, 2007, Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data, Journal of Machine Learning Research, 8, 693
  34. Taskar, 2004, Advances in Neural Information Processing Systems 16
  35. Wainwright, 2006, Advances in Neural Information Processing Systems 18
  36. Welling, 2006, Bayesian random fields: The Bethe-Laplace approximation
  37. Wick, 2009, SampleRank: Learning preferences from atomic gradients
  38. Wick, 2008, A discriminative approach to ontology alignment
  39. Yu, 2010, A quasiNewton approach to nonsmooth convex optimization problems in machine learning, Journal of Machine Learning Research, 11, 1145
  40. Zhang, 2011, Quasi-Newton Markov chain Monte Carlo

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Metadata

Authors
Sutton, C., & McCallum, A.
Venue
Foundations and Trends® in Machine Learning
Publisher
Emerald
Published
2012-08-23
Volume
4
Issue
4
Pages
267-373
ISSN
  • 1935-8237 (ISSN-L)
  • 1935-8245
DOI
10.1561/2200000013
OpenAlex
W2129999749
Cited by
564
References
136
FWCI
43.06
Type
journal article
Language
en
Open access
green

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