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ICML
2004
IEEE

Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

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Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longrange dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linear-chain conditional random fields (CRFs) in which each time slice contains a set of state variables and edges--a distributed state representation as in dynamic Bayesian networks (DBNs)--and parameters are tied across slices. Since exact inference can be intractable in such models, we perform approximate inference using several schedules for belief propagation, including tree-based reparameterization (TRP). On a natural-language chunking task, we show that a DCRF performs better than a series of linearchain CRFs, achieving comparable performance using only half the training data.
Charles A. Sutton, Khashayar Rohanimanesh, Andrew
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Charles A. Sutton, Khashayar Rohanimanesh, Andrew McCallum
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