Inference & Learning with Linear Constraints
We present a discriminatory learning framework for the prob lem of assigning globally optimal values to a set of variables with complex an d expressive dependencies among them. The problem is modeled as an integer l inea program (ILP) where the cost values associated with the variables ar represented and trained as linear classifiers. The framework unifies and exte nds several existing discriminatory approaches; most importantly, it su pports more complex dependencies among variables than existing ones. This pres entation concentrates on the benefits of the additional expressivity and on c omparing different training paradigms – with and without global feedback – in th e context of semantic role labeling.