Difference between revisions of "Class Meeting for 10-710 09-08-2011"
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* [http://jmlr.csail.mit.edu/papers/volume9/dietterich08a/dietterich08a.pdf Gradient tree boosting for training CRFs, Dietterich et al, ICML 2004]. A very different training method for CRFs, based on regression trees. | * [http://jmlr.csail.mit.edu/papers/volume9/dietterich08a/dietterich08a.pdf Gradient tree boosting for training CRFs, Dietterich et al, ICML 2004]. A very different training method for CRFs, based on regression trees. | ||
* [http://acl.ldc.upenn.edu/P/P06/P06-1027.pdf Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling, Jiao et al, ACL 2006]. A very nice paper from the UofA group on semi-supervised CRF learning. | * [http://acl.ldc.upenn.edu/P/P06/P06-1027.pdf Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling, Jiao et al, ACL 2006]. A very nice paper from the UofA group on semi-supervised CRF learning. | ||
| − | * [http:// | + | * [http://www.cs.ubc.ca/~murphyk/Papers/icml06_camera.pdf Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods, Vishwanathan et al, ICML 2006]. CRF learning methods seem complicated - this paper shows that stochastic gradient methods, a class of very simple on-line methods, can be competitive. |
* Choi, Y., and C. Cardie. Hierarchical Sequential Learning for Extracting Opinions and their Attributes. ACL-2010 (short paper) | * Choi, Y., and C. Cardie. Hierarchical Sequential Learning for Extracting Opinions and their Attributes. ACL-2010 (short paper) | ||
* Lavergne, T., O. Cappé, T. ParisTech, and F. Yvon. Practical very large scale CRFs. ACL-2010. Full of detailed implementation-oriented tricks. | * Lavergne, T., O. Cappé, T. ParisTech, and F. Yvon. Practical very large scale CRFs. ACL-2010. Full of detailed implementation-oriented tricks. | ||
Revision as of 11:12, 8 September 2011
This is one of the class meetings on the schedule for the course Syllabus for Structured Prediction 10-210 in Fall 2011.
Linear-chain CRFs
- Slides
- Supplement to Sha & Pereira's paper - a more detailed derivation of the CRF gradient.
Required Readings
I will also cover most of the material in the paper below in lecture. This paper defines CRFs somewhat more generally than Sha & Pereira do.
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Lafferty et al, 2001. The original CRF paper.
Optional Readings
- Hidden Markov Models for Labeled Sequences, Krogh 1994. The method of this paper appears to be equivalent to linear-chain CRFs - so why didn't it catch on?
- Gradient tree boosting for training CRFs, Dietterich et al, ICML 2004. A very different training method for CRFs, based on regression trees.
- Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling, Jiao et al, ACL 2006. A very nice paper from the UofA group on semi-supervised CRF learning.
- Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods, Vishwanathan et al, ICML 2006. CRF learning methods seem complicated - this paper shows that stochastic gradient methods, a class of very simple on-line methods, can be competitive.
- Choi, Y., and C. Cardie. Hierarchical Sequential Learning for Extracting Opinions and their Attributes. ACL-2010 (short paper)
- Lavergne, T., O. Cappé, T. ParisTech, and F. Yvon. Practical very large scale CRFs. ACL-2010. Full of detailed implementation-oriented tricks.
Background
- An Introduction to Conditional Random Fields for Relational Learning. A longish tutorial overview of CRFs.
