Conditional Random Fields

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Presentation transcript:

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

Motivation Observed variables X Target variables Y

CRF Representation

CRFs and Logistic Model draw structure, show conditional distribution

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

More CRFs for Language Different chains can use different features

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