  We present an objective function for learning with unlabeled data that
utilizes auxiliary expectation constraints. We optimize this objective function
using a procedure that alternates between information and moment projections.
Our method provides an alternate interpretation of the posterior regularization
framework (Graca et al., 2008), maintains uncertainty during optimization
unlike constraint-driven learning (Chang et al., 2007), and is more efficient
than generalized expectation criteria (Mann & McCallum, 2008). Applications of
this framework include minimally supervised learning, semisupervised learning,
and learning with constraints that are more expressive than the underlying
model. In experiments, we demonstrate comparable accuracy to generalized
expectation criteria for minimally supervised learning, and use expressive
structural constraints to guide semi-supervised learning, providing a 3%-6%
improvement over stateof-the-art constraint-driven learning.
