Conditional Random Fields and Graphical Models

I am currently reading into the topic of Conditional Random Fields, Hidden Markov Models in particular and Graphical Models in general. A perfect tutorial on this topics is provided by Sutton and McCallum [1]. It is more than readworthy and gives

• a basic introduction to relational learning for graphical models
• an overview on CRF and HMM as well as their application
  
• Generative vs. descriptive models (i.e. Naive Bayes vs. Linear Regression)
• Parameter estimation, backward-forward estimation and application of gneral and linear chain CRF's
• Application of general CRF's (i.e. skip chain CRF) for information extraction

A must read for everybody interested in information extraction and relational learning state-of-the-art

[1] Introduction to Conditional Random Fields for Relational Learning

Charles Sutton and Andrew Mccallum MIT Press, (2006)