Difference between revisions of "Class Meeting for 10-710 09-27-2011"
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=== Meta-Learning: Stacking and Sequential Models === | === Meta-Learning: Stacking and Sequential Models === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10- | + | * [http://www.cs.cmu.edu/~wcohen/10-710/09-27-stacking+searn.ppt Slides in PowerPoint] |
| − | * [http://www.cs.cmu.edu/~wcohen/10- | + | * [http://www.cs.cmu.edu/~wcohen/10-710/09-27-stacking+searn.pdf Slides in PDF] |
=== Required Readings === | === Required Readings === | ||
Latest revision as of 11:33, 28 September 2011
This is one of the class meetings on the schedule for the course Syllabus for Structured Prediction 10-210 in Fall 2011.
Meta-Learning: Stacking and Sequential Models
Required Readings
- Stacked sequential learning, by William W. Cohen, Vitor Carvalho. In International Joint Conference on Artificial Intelligence, 2005..
- Search-Based Structured Prediction, Dame, Langford and Marcu. This is a journal-length paper, but it's not very dense. We will not be going through the theorems in any detail.
- Searn in Practice Unpublished manuscript, Daume et al.
Optional Readings
- An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition, Krishnan and Manning, ACL 2006. Another take on stacked sequential learning.
- Zhenzhen Kou and William W. Cohen (2007): Stacked Graphical Models for Efficient Inference in Markov Random Fields in SDM-2007. Extended version of the stacked sequential learning method that applies to arbitrary graphs.
- Transformation-Based Error-Driven Learning and Natural Language Processing, Brill, COLING 1995. The learning algorithm in the Brill parser, which has also been used for NER (e.g., in Abgene).
- Conditional graphical models, Perez-Cruz & Ghahramani, 2007, in Predicting Structured Data. MIT Press, Cambridge, MA, USA, pp. 265-282.. A very simple and effective meta-learning method.
- Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction An alternative formal analysis of Searn.
- Unsupervised Search-based Structured Prediction. Applying Searn to unsupervised and/or semi-supervised problems.
- Stacking Dependency Parsers. What the title says.
