Difference between revisions of "Class Meeting for 10-707 9/27/2010"
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=== 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}}]]. |
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=== Optional Readings === | === Optional Readings === | ||
Latest revision as of 10:38, 27 September 2010
This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2010.
Meta-Learning: Stacking and Sequential Models
The notes also have a short review of last week's session on CRFs.
- Slides
- Additional notes on Sha & Pereira, including derivation of the gradient of the loglikelihood for CRFs].
Required Readings
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.
