Difference between revisions of "Class Meeting for 10-710 10-06-2011"
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* [http://research.microsoft.com/pubs/73694/RothYi05.pdf Integer Linear Programming Inference for Conditional Random Fields], D. Roth and W.-T. Yih, ICML 2005 | * [http://research.microsoft.com/pubs/73694/RothYi05.pdf Integer Linear Programming Inference for Conditional Random Fields], D. Roth and W.-T. Yih, ICML 2005 | ||
* [http://aclweb.org/anthology-new/N/N06/N06-1054.pdf A Fast Finite-State Relaxation Method for Enforcing Global Constraints on Sequence Decoding], R. W. Tromble and J. Eisner, NAACL 2006 (this paper is not about using ILP; it shows that the ILP of the paper above can be better represented using finite-state machines and a blend of dynamic programming and cutting planes-style relaxation) | * [http://aclweb.org/anthology-new/N/N06/N06-1054.pdf A Fast Finite-State Relaxation Method for Enforcing Global Constraints on Sequence Decoding], R. W. Tromble and J. Eisner, NAACL 2006 (this paper is not about using ILP; it shows that the ILP of the paper above can be better represented using finite-state machines and a blend of dynamic programming and cutting planes-style relaxation) | ||
| + | * [http://aclweb.org/anthology-new/N/N06/N06-1015.pdf Word Alignment via Quadratic Assignment], S. Lacoste-Julien, B. Taskar, D. Klein, and M. I. Jordan, NAACL 2006 | ||
* Also see this [http://ilpnlp.wikidot.com/bibliography bibliography] of uses of ILP for natural language processing through 2008 | * Also see this [http://ilpnlp.wikidot.com/bibliography bibliography] of uses of ILP for natural language processing through 2008 | ||
=== Background Readings === | === Background Readings === | ||
| + | |||
| + | * When decoding/inference can be cast as an LP, there are some interesting approaches to learning in the max-margin framework: [http://www.jmlr.org/papers/volume7/taskar06a/taskar06a.pdf Structured Prediction, Dual Extragradient and Bregman Projections], B. Taskar, S. Lacoste-Julien, and M. I. Jordan, JMLR 7:1627-1653 | ||
| + | * Embedding LP relaxed inference inside learning, and encouraging your model to avoid fractional vertices: [http://www.cs.cmu.edu/~nasmith/papers/martins+smith+xing.icml09.pdf Polyhedral Outer Approximations with Application to Natural Language Parsing], A. F. T. Martins, N. A. Smith, E. P. Xing, ICML 2009 | ||
Revision as of 21:43, 5 October 2011
This is one of the class meetings on the schedule for the course Structured Prediction 10-710 in Fall 2011.
Contents
Making Structured Predictions with Integer Linear Programming
Required Readings
- Section 2.2.2 of Linguistic Structure Prediction, Smith 2011 (assuming you read the rest of the chapter for Tuesday)
Optional Readings
- A Linear Programming Formulation for Global Inference in Natural Language Tasks, D. Roth and W.-T. Yih, CoNLL 2004
- Integer Linear Programming Inference for Conditional Random Fields, D. Roth and W.-T. Yih, ICML 2005
- A Fast Finite-State Relaxation Method for Enforcing Global Constraints on Sequence Decoding, R. W. Tromble and J. Eisner, NAACL 2006 (this paper is not about using ILP; it shows that the ILP of the paper above can be better represented using finite-state machines and a blend of dynamic programming and cutting planes-style relaxation)
- Word Alignment via Quadratic Assignment, S. Lacoste-Julien, B. Taskar, D. Klein, and M. I. Jordan, NAACL 2006
- Also see this bibliography of uses of ILP for natural language processing through 2008
Background Readings
- When decoding/inference can be cast as an LP, there are some interesting approaches to learning in the max-margin framework: Structured Prediction, Dual Extragradient and Bregman Projections, B. Taskar, S. Lacoste-Julien, and M. I. Jordan, JMLR 7:1627-1653
- Embedding LP relaxed inference inside learning, and encouraging your model to avoid fractional vertices: Polyhedral Outer Approximations with Application to Natural Language Parsing, A. F. T. Martins, N. A. Smith, E. P. Xing, ICML 2009
