Now that we have seen sequence models, in this lecture, we will generalize the techniques we saw to arbitrary structured outputs.
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.
- Dan Klein and Christopher D. Manning, Conditional Structure versus Conditional Estimation in NLP Models
Surveys of formulations for structured prediction
Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields for Relational Learning.
(*) Ming-Wei Chang, Lev Ratinov and Dan Roth, Structured learning with constrained conditional models, Machine Learning, 2012.
Chapter 5 of Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction in Computer Vision on Conditional Random Fields.
(*) Mark Richardson and Pedro Domingos, Markov logic networks, Machine Learning, 2006.