Inference & Learning with Linear Constraints
نویسندگان
چکیده
We present a discriminatory learning framework for the problem of assigning globally optimal values to a set of variables with complex and expressive dependencies among them. The problem is modeled as an integer linear program (ILP) where the cost values associated with the variables are represented and trained as linear classifiers. The framework unifies and extends several existing discriminatory approaches; most importantly, it supports more complex dependencies among variables than existing ones. This presentation concentrates on the benefits of the additional expressivity and on comparing different training paradigms – with and without global feedback – in the context of semantic role labeling.
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