This lecture covers the use of constraints to drive learning. Doing so gives us a clean way to encode background knowledge into our models.
Lectures and readings
-
(*) Ming-Wei Chang, Lev Ratinov, and Dan Roth, Guiding Semi-Supervision with Constraint-Driven Learning ACL 2007.
-
(*) Gideon Mann and Andrew McCallum, Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields, ACL 2008.
-
(*) Kuzman Ganchev, João Graça, Jennifer Gillenwater and Ben Taskar, Posterior Regularization for Structured Latent Variable Models, JMLR, 2010.