Difference between revisions of "Roth and Yih, ICML 2005. Integer Linear Programming Inference for Conditional Random Fields"
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== Summary == | == Summary == | ||
| − | This [[Category::paper]] presents an alternative approach to inference in conditional random fields using [[UsesMethod::Integer Linear Programming | integer linear programming]] (ILP). | + | This [[Category::paper]] presents an alternative approach to inference in [[UsesMethod::Conditional Random Fields | conditional random fields]] using [[UsesMethod::Integer Linear Programming | integer linear programming]] (ILP). The standard Viterbi algorithm based on dynamic programming, in general, cannot efficiently incorporate non-local and non-sequential constraints over the output sequence. The authors propose an ILP-based method to inference procedure. and extend CRF models to naturally and efficiently support general constraint structures. |
== Method == | == Method == | ||
Revision as of 17:44, 30 October 2011
Citation
Dan Roth and Wen-tau Yih. 2005. Integer Linear Programming Inference for Conditional Random Fields. In Proceedings of the 22^nd International Conference on Machine learning, ICML'05, New York, NY, USA.
Online Version
Summary
This paper presents an alternative approach to inference in conditional random fields using integer linear programming (ILP). The standard Viterbi algorithm based on dynamic programming, in general, cannot efficiently incorporate non-local and non-sequential constraints over the output sequence. The authors propose an ILP-based method to inference procedure. and extend CRF models to naturally and efficiently support general constraint structures.
