Dynamic conditional random fields

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 long-range 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…

• NS
  National Science Foundation

  Award: 0326249
• UD
  U.S. Department of the Interior

  Award: NBCHD030010
• NS
  National Security Agency
• DA
  Defense Advanced Research Projects Agency

  Award: NBCHD030010
• AR
  Advanced Research Projects Agency