Constraints as Prior Knowledge

author: Dan Roth, Harvard University

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*Slides*
0:00	Constraints as Prior Knowledge
0:18	Tasks of Interest
1:56	Task of Interests: Structured Output
2:23	Information Extraction via Hidden Markov Models
2:55	Strategies for Improving the Results
3:54	Information extraction without Prior Knowledge
4:07	Examples of Constraints
4:37	Information Extraction with Constraints
4:56	Information Extraction with Constraints
6:29	Outline - Constrained Conditional Model
6:50	Constrained Conditional Models
9:27	Features Versus Constraints
11:12	- Questions
13:39	Constraints and Inference
14:52	Outline - Constrained Conditional Model: Training
14:54	Training Strategies
16:04	Factored (L+I) Approaches
16:25	Joint Approaches
16:48	Outline - Constrained Conditional Model: Semi-supervised Learning
16:51	Semi-supervised Learning with Constraints
18:05	Outline - Results
18:07	Results on Factored Model - Citations
18:55	Results on Factored Model - Advertisements
19:23	Hard Constraints vs. Weighted Constraints
19:51	Factored vs. Jointed Training
21:01	Value of Constraints in Semi-Supervised Learning
21:30	Summary: Constrained Conditional Models
22:22	Discussion
22:41	- Questions

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