1 An Introduction to Conditional Random Fields for ...

1 An Introduction to Conditional RandomFields for Relational LearningCharles SuttonDepartment of Computer ScienceUniversity of Massachusetts, casuttonAndrew McCallumDepartment of Computer ScienceUniversity of Massachusetts, IntroductionRelational data has two characteristics: first, statistical dependencies exist betweenthe entities we wish to model, and second, each entity often has a rich set of featuresthat can aid classification. For example, when classifying Web documents, thepage s text provides much information about the class label, but hyperlinks definea relationship between pages that can improve classification [Taskar et al., 2002].Graphical models are a natural formalism for exploiting the dependence structureamong entities. Traditionally, graphical models have been used to represent thejoint probability distributionp(y,x), where the variablesyrepresent the attributesof the entities that we wish to predict, and the input variablesxrepresent ourobserved knowledge about the entities.

1.2 Graphical Models 5 nonzero only for a single class. To do this, the feature functions can be defined as f y0,j(y,x) = 1 {0= }x j for the feature weights and f y0(y,x) = 1 for the bias weights. Now we can use f k to index each feature function f y0,j, and λ k to index its corresponding weight λ

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  Random, Conditional, Conditional random

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