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Conditional Random Fields

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Presentation on theme: "Conditional Random Fields"— Presentation transcript:

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

2 Motivation Observed variables X Target variables Y

3 CRF Representation

4 CRFs and Logistic Model
draw structure, show conditional distribution

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

6 More CRFs for Language Different chains can use different features

7 Summary A CRF is parameterized the same as a Gibbs distribution, but normalized differently 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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