Class Meeting for 10-710 09-27-2011
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Revision as of 10:45, 25 July 2011 by Wcohen (talk | contribs) (moved Class Meeting for 10-710 9-27-2011 to Class Meeting for 10-710 09-27-2011)
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- Searn in Practice Unpublished manuscript, Daume et al.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).
- Search-based Structured Prediction, Daume, Langford, and Marcu, Machine Learning Journal (2009). Another clever meta-learning algorithm that works well for sequences.
- 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.
