Difference between revisions of "Class Meeting for 10-710 09-08-2011"
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* [[required::sha_2003_shallow_parsing_with_conditional_random_fields | {{MyCiteconference| booktitle = Proceedings of HLT-NAACL| coauthors = F. Pereira| date = 2003| first = F.| last = Sha| pages = 213–220| title = Shallow parsing with conditional random fields}}]] | * [[required::sha_2003_shallow_parsing_with_conditional_random_fields | {{MyCiteconference| booktitle = Proceedings of HLT-NAACL| coauthors = F. Pereira| date = 2003| first = F.| last = Sha| pages = 213–220| title = Shallow parsing with conditional random fields}}]] | ||
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| + | 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. | ||
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| + | * [http://www.cis.upenn.edu/~pereira/papers/crf.pdf Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Lafferty et al, 2001]. The original CRF paper. | ||
=== Optional Readings === | === Optional Readings === | ||
Revision as of 12:46, 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
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. 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.
