# Class Meeting for 10-707 9/22/2010

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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.