Difference between revisions of "Class Meeting for 10-710 09-27-2011"
From Cohen Courses
Jump to navigationJump to search (Created page with 'This is one of the class meetings on the schedule for the course [[Syllabus for Structured Prediction 10-710 in Fall 20…') |
|||
| (8 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
This is one of the class meetings on the [[Syllabus for Structured Prediction 10-710 in Fall 2011|schedule]] for the course [[Syllabus for Structured Prediction 10-710 in Fall 2011|Syllabus for Structured Prediction 10-210 in Fall 2011]]. | This is one of the class meetings on the [[Syllabus for Structured Prediction 10-710 in Fall 2011|schedule]] for the course [[Syllabus for Structured Prediction 10-710 in Fall 2011|Syllabus for Structured Prediction 10-210 in Fall 2011]]. | ||
| − | |||
=== Meta-Learning: Stacking and Sequential Models === | === Meta-Learning: Stacking and Sequential Models === | ||
| − | + | * [http://www.cs.cmu.edu/~wcohen/10-710/09-27-stacking+searn.ppt Slides in PowerPoint] | |
| − | + | * [http://www.cs.cmu.edu/~wcohen/10-710/09-27-stacking+searn.pdf Slides in PDF] | |
| − | * [http://www.cs.cmu.edu/~wcohen/10- | ||
=== Required Readings === | === Required Readings === | ||
* [[required::cohen_2005_stacked_sequential_learning | {{MyCiteconference| booktitle = International Joint Conference on Artificial Intelligence| coauthors = Vitor Carvalho| date = 2005| first = William W.| last = Cohen| pages = 671-676| title = Stacked sequential learning}}]]. | * [[required::cohen_2005_stacked_sequential_learning | {{MyCiteconference| booktitle = International Joint Conference on Artificial Intelligence| coauthors = Vitor Carvalho| date = 2005| first = William W.| last = Cohen| pages = 671-676| title = Stacked sequential learning}}]]. | ||
| + | * [http://hal3.name/docs/daume09searn.pdf 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. | ||
| + | * [http://hal3.name/docs/daume06searn-practice.pdf Searn in Practice] Unpublished manuscript, Daume et al. | ||
=== Optional Readings === | === Optional Readings === | ||
| Line 17: | Line 17: | ||
* [http://www-2.cs.cmu.edu/~wcohen/postscript/sdm-2007-stack.pdf 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. | * [http://www-2.cs.cmu.edu/~wcohen/postscript/sdm-2007-stack.pdf 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. | ||
* [http://www.cs.jhu.edu/~brill/CompLing95.ps 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). | * [http://www.cs.jhu.edu/~brill/CompLing95.ps 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). | ||
| − | |||
* [http://learning.eng.cam.ac.uk/zoubin/papers/CGM.pdf 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. | * [http://learning.eng.cam.ac.uk/zoubin/papers/CGM.pdf 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. | ||
| + | * [http://hal3.name/docs/daume05laso.pdf Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction] An alternative formal analysis of Searn. | ||
| + | * [http://hal3.name/docs/daume09unsearn.pdf Unsupervised Search-based Structured Prediction]. Applying Searn to unsupervised and/or semi-supervised problems. | ||
| + | * [http://www.cs.cmu.edu/~nasmith/papers/martins+das+smith+xing.emnlp08.pdf Stacking Dependency Parsers]. What the title says. | ||
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.
