Difference between revisions of "Class Meeting for 10-707 9/22/2010"
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* [http://www.cs.cmu.edu/~wcohen/10-707/09-22-crfs.ppt Slides] | * [http://www.cs.cmu.edu/~wcohen/10-707/09-22-crfs.ppt Slides] | ||
| + | * [http://www.cs.cmu.edu/~wcohen/10-707/crf-notes/crf-update.pdf Supplement to Sha & Pereira's paper] - a more detailed derivation of the CRF gradient. | ||
=== Required Readings === | === Required Readings === | ||
| Line 16: | Line 17: | ||
* [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://delivery.acm.org/10.1145/1150000/1143966/p969-vishwanathan.pdf?key1=1143966&key2=9755563521&coll=GUIDE&dl=GUIDE&CFID=54241528&CFTOKEN=10662550 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. | * [http://delivery.acm.org/10.1145/1150000/1143966/p969-vishwanathan.pdf?key1=1143966&key2=9755563521&coll=GUIDE&dl=GUIDE&CFID=54241528&CFTOKEN=10662550 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. ractical very large scale CRFs. ACl-2010. Full of detailed implementation-oriented tricks. | ||
=== Background === | === Background === | ||
Latest revision as of 12:44, 24 September 2010
This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2010.
Linear-chain CRFs
- Slides
- Supplement to Sha & Pereira's paper - a more detailed derivation of the CRF gradient.
Required Readings
- Shallow parsing with conditional random fields, by F. Sha, F. Pereira. In Proceedings of HLT-NAACL, 2003.
- Conditional structure versus conditional estimation in NLP models, by D. Klein, C. D Manning. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 2002.
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. ractical very large scale CRFs. ACl-2010. Full of detailed implementation-oriented tricks.
Background
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Lafferty et al, 2001. The original CRF paper.
- An Introduction to Conditional Random Fields for Relational Learning. A longish tutorial overview of CRFs.
