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Conditional Random Fields

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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.

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Conditional Random Fields

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  1. 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

  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

  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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