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Daphne Koller Markov Networks Conditiona l Random Fields Probabilistic Graphical Models Representation

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Representation. Probabilistic Graphical Models. Markov Networks. Conditional Random Fields. Motivation. Observed variables X Target variables Y. CRF Representation. CRFs and Logistic Model. CRFs for Language. - PowerPoint PPT Presentation

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Page 1: Conditional Random Fields

Daphne Koller

Markov Networks

Conditional Random Fields

ProbabilisticGraphicalModels

Representation

Page 2: Conditional Random Fields

Daphne Koller

Motivation• Observed variables X• Target variables Y

Page 3: Conditional Random Fields

Daphne Koller

CRF Representation

Page 4: Conditional Random Fields

Daphne Koller

CRFs and Logistic Model

Page 5: Conditional Random Fields

Daphne Koller

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

CRFs for Language

Page 6: Conditional Random Fields

Daphne Koller

More CRFs for Language

Page 7: Conditional Random Fields

Daphne Koller

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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