Difference between revisions of "Class Meeting for 10-710 09-08-2011"
From Cohen Courses
Jump to navigationJump to search| Line 21: | Line 21: | ||
=== Background === | === Background === | ||
| − | + | ||
* [http://www.cs.umass.edu/~mccallum/papers/crf-tutorial.pdf An Introduction to Conditional Random Fields for Relational Learning]. A longish tutorial overview of CRFs. | * [http://www.cs.umass.edu/~mccallum/papers/crf-tutorial.pdf An Introduction to Conditional Random Fields for Relational Learning]. A longish tutorial overview of CRFs. | ||
Revision as of 12:44, 7 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
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
- An Introduction to Conditional Random Fields for Relational Learning. A longish tutorial overview of CRFs.
