与最大熵模型相似，条件随机场（Conditional random fields，CRFs）是一种机器学习模型，在自然语言处理的许多领域（如词性标注、中文分词、命名实体识别等）都有比较好的应用效果。条件随机场最早由John D. Lafferty提出，其也是Brown90的作者之一，和贾里尼克相似，在离开IBM后他去了卡耐基梅隆大学继续搞学术研究，2001年以第一作者的身份发表了CRF的经典论文 “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”。
关于条件随机场的参考文献及其他资料，Hanna Wallach在05年整理和维护的这个页面“conditional random fields”非常不错，其中涵盖了自01年CRF提出以来的很多经典论文（不过似乎只到05年，之后并未更新）以及几个相关的工具包(不过也没有包括CRF++），但是仍然非常值得入门条件随机场的读者参考，以下摘选自该网页。
Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.
Hanna M. Wallach. Conditional Random Fields: An Introduction. Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.
John Lafferty, Andrew McCallum, Fernando Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001), 2001.
Hanna Wallach. Efficient Training of Conditional Random Fields. M.Sc. thesis, Division of Informatics, University of Edinburgh, 2002.
Thomas G. Dietterich. Machine Learning for Sequential Data: A Review. In Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, Vol. 2396, T. Caelli (Ed.), pp. 15–30, Springer-Verlag, 2002.
Fei Sha and Fernando Pereira. Shallow Parsing with Conditional Random Fields. In Proceedings of the 2003 Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT/NAACL-03), 2003.
Andrew McCallum. Efficiently Inducing Features of Conditional Random Fields. In Proceedings of the 19th Conference in Uncertainty in Articifical Intelligence (UAI-2003), 2003.
David Pinto, Andrew McCallum, Xing Wei and W. Bruce Croft. Table Extraction Using Conditional Random Fields. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003), 2003.
Andrew McCallum and Wei Li. Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons. In Proceedings of the Seventh Conference on Natural Language Learning (CoNLL), 2003.
Wei Li and Andrew McCallum. Rapid Development of Hindi Named Entity Recognition Using Conditional Random Fields and Feature Induction. In ACM Transactions on Asian Language Information Processing (TALIP), 2003.
Yasemin Altun and Thomas Hofmann. Large Margin Methods for Label Sequence Learning. In Proceedings of 8th European Conference on Speech Communication and Technology (EuroSpeech), 2003.
Simon Lacoste-Julien. Combining SVM with graphical models for supervised classification: an introduction to Max-Margin Markov Networks. CS281A Project Report, UC Berkeley, 2003.
Andrew McCallum, Khashayar Rohanimanesh and Charles Sutton. Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences. Workshop on Syntax, Semantics, Statistics; 16th Annual Conference on Neural Information Processing Systems (NIPS 2003), 2004.
Kevin Murphy, Antonio Torralba and William T.F. Freeman. Using the forest to see the trees: a graphical model relating features, objects and scenes. In Advances in Neural Information Processing Systems 16 (NIPS 2003), 2004.
Sanjiv Kumar and Martial Hebert. Discriminative Fields for Modeling Spatial Dependencies in Natural Images. In Advances in Neural Information Processing Systems 16 (NIPS 2003), 2004.
Ben Taskar, Carlos Guestrin and Daphne Koller. Max-Margin Markov Networks. In Advances in Neural Information Processing Systems 16 (NIPS 2003), 2004.
Burr Settles. Biomedical Named Entity Recognition Using Conditional Random Fields and Rich Feature Sets. To appear in Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA), 2004.
A demo of the system can be downloaded here.
Charles Sutton, Khashayar Rohanimanesh and Andrew McCallum. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 2004.
John Lafferty, Xiaojin Zhu and Yan Liu. Kernel conditional random fields: representation and clique selection. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 2004.
Xuming He, Richard Zemel, and Miguel Á. Carreira-Perpiñán. Multiscale conditional random fields for image labelling. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), 2004.
Yasemin Altun, Alex J. Smola, Thomas Hofmann. Exponential Families for Conditional Random Fields. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (UAI-2004), 2004.
Michelle L. Gregory and Yasemin Altun. Using Conditional Random Fields to Predict Pitch Accents in Conversational Speech. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), 2004.
Brian Roark, Murat Saraclar, Michael Collins and Mark Johnson. Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), 2004.
Ryan McDonald and Fernando Pereira. Identifying Gene and Protein Mentions in Text Using Conditional Random Fields. BioCreative, 2004.
Trausti T. Kristjansson, Aron Culotta, Paul Viola and Andrew McCallum. Interactive Information Extraction with Constrained Conditional Random Fields. In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI 2004), 2004.
Thomas G. Dietterich, Adam Ashenfelter and Yaroslav Bulatov. Training Conditional Random Fields via Gradient Tree Boosting. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 2004.
John Lafferty, Yan Liu and Xiaojin Zhu. Kernel Conditional Random Fields: Representation, Clique Selection, and Semi-Supervised Learning. Technical Report CMU-CS-04-115, Carnegie Mellon University, 2004.
Fuchun Peng and Andrew McCallum (2004). Accurate Information Extraction from Research Papers using Conditional Random Fields. In Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT/NAACL-04), 2004.
Yasemin Altun, Thomas Hofmann and Alexander J. Smola. Gaussian process classification for segmenting and annotating sequences. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 2004.
Yasemin Altun and Thomas Hofmann. Gaussian Process Classification for Segmenting and Annotating Sequences. Technical Report CS-04-12, Department of Computer Science, Brown University, 2004.
Cristian Smimchisescu, Atul Kanaujia, Zhiguo Li and Dimitris Metaxus. Conditional Models for Contextual Human Motion Recognition. In Proceedings of the International Conference on Computer Vision, (ICCV 2005), Beijing, China, 2005.
Ariadna Quattoni, Michael Collins and Trevor Darrel. Conditional Random Fields for Object Recognition. In Advances in Neural Information Processing Systems 17 (NIPS 2004), 2005.
Jospeh Bockhorst and Mark Craven. Markov Networks for Detecting Overlapping Elements in Sequence Data. In Advances in Neural Information Processing Systems 17 (NIPS 2004), 2005.
Antonio Torralba, Kevin P. Murphy, William T. Freeman. Contextual models for object detection using boosted random fields. In Advances in Neural Information Processing Systems 17 (NIPS 2004), 2005.
Sunita Sarawagi and William W. Cohen. Semi-Markov Conditional Random Fields for Information Extraction. In Advances in Neural Information Processing Systems 17 (NIPS 2004), 2005.
Yuan Qi, Martin Szummer and Thomas P. Minka. Bayesian Conditional Random Fields. To appear in Proceedings of the Tenth International W\orkshop on Artificial Intelligence and Statistics (AISTATS 2005), 2005.
Aron Culotta, David Kulp and Andrew McCallum. Gene Prediction with Conditional Random Fields. Technical Report UM-CS-2005-028. University of Massachusetts, Amherst, 2005.
Yang Wang and Qiang Ji. A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Volume 1, 2005.
An Introduction to Conditional Random Fields. Charles Sutton, Andrew McCallum. Foundations and Trends in Machine Learning. To appear. 2011.
MALLET: A Machine Learning for Language Toolkit.
MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text.
ABNER: A Biomedical Named Entity Recognizer.
ABNER is a text analysis tool for molecular biology. It is essentially an interactive, user-friendly interface to a system designed as part of the NLPBA/BioNLP 2004 Shared Task challenge.
MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text.
Conditional random fields (chains, trees and general graphs; includes BP code).
The CRF package is a Java implementation of conditional random fields for sequential labeling.
最后推荐CRF++:Yet Another CRF toolkit，如果读者对于基于字标注的中文分词感兴趣，可以很快的利用该工具包构造一个基于条件随机场的中文分词工具，而且性能也不赖。