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Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Sutton, Charles Rohanimanesh, Khashayar Mccallum, Andrew |
| Description | 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 In ICML |
| File Format | |
| Language | English |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Time Slice Complex Interaction Distributed State Representation Factorized Probabilistic Model Present Dynamic Conditional Random Field Dynamic Bayesian Network Sequence Modeling State Variable Longrange Dependency Linear-chain Conditional Random Field Segmenting Sequence Data Dynamic Conditional Random Field |
| Content Type | Text |
| Resource Type | Article |
