1. Conditional Random Fields
Representation
Probabilistic
Graphical
Models
Markov Networks
Conditional Random Fields

2. Task-specific predictionX Y
Image
segmentation
Text
processing

3. Correlated Features Ci
Xi1
...
Xik
color & texture
histograms

4. CRF Representation

5. CRFs and Logistic Model
draw structure, show conditional distribution

6. CRFs for Language
Features: word capitalized, word in atlas or name list, previous word is “Mrs”, next word is “Times”, …

7. More CRFs for Language
Different chains can use different features

8. Summary
A CRF is parameterized the same as a Gibbs distribution, but normalized differently
Generalizes logistic regression models
Don’t need to model distribution over variables we don’t care about
Allows models with highly expressive features, without worrying about wrong independencies

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