Best resource to learn about conditional random fields?

Also, from the text version of Charles Elkan's lectures :

The following are four tutorials that are available on the web.

1. Hanna M. Wallach. Conditional Random Fields: An Introduction. Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.
2. Charles Sutton and Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning. In Introduction to Statistical Relational Learning. Edited by Lise Getoor and Ben Taskar. MIT Press, 2006.
   
3. Rahul Gupta. Conditional Random Fields. Unpublished report, IIT Bombay, 2006.
4. Roland Memisevic. An Introduction to Structured Discriminative Learning. Technical Report, University of Toronto, 2006.
   

All four surveys above are very good. The report by Memisevic places CRFs in the context of other methods for learning to predict complex outputs, especially SVMinspired large-margin methods. Sutton’s survey is a longer discussion, with many helpful comments and explanations. The tutorial by Wallach is easy to follow and provides high-level intuition. One difference between the two tutorials is that Wallach represents CRFs as undirected graphical models, whereas Sutton uses undirected factor graphs. Sutton also does parallel comparisons of naive Bayes (NB) and logistic regression, and of hidden Markov models (HMMs) and linearchain CRFs. This gives readers a useful starting point if they have experience with NB classifiers or HMMs. Gupta’s paper gives a detailed derivation of the important equations for CRFs.