Now that we have seen sequence models, in this lecture, we will generalize the techniques we saw to arbitrary structured outputs.
Lecture
Readings
Background on Graphical Models
- Daphne Koller, Nir Friedman and Ben Taskar, Graphical Models in a Nutshell
- Chapter 19 of Kevin Murphy, Undirected graphical models (Markov random fields) (available online).
- Chapter 2 of Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction in Computer Vision.
Conditional models
- Dan Klein and Christopher D. Manning, Conditional Structure versus Conditional Estimation in NLP Models
Surveys of formulations for structured prediction
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Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields for Relational Learning.
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(*) Ming-Wei Chang, Lev Ratinov and Dan Roth, Structured learning with constrained conditional models, Machine Learning, 2012.
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Chapter 5 of Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction in Computer Vision on Conditional Random Fields.
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(*) Mark Richardson and Pedro Domingos, Markov logic networks, Machine Learning, 2006.