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Collective graphical models exploit inter-instance associative dependence tooutput more accurate labelings. However existing models support very limitedkind of associativity which restricts accuracy gains. This paper makes twomajor contributions. First, we propose a general collective inference frameworkthat biases data instances to agree on a set of {\em properties} of theirlabelings. Agreement is encouraged through symmetric clique potentials. We showthat rich properties leads to bigger gains, and present a systematic inferenceprocedure for a large class of such properties. The procedure performs messagepassing on the cluster graph, where property-aware messages are computed withcluster specific algorithms. This provides an inference-only solution fordomain adaptation. Our experiments on bibliographic information extractionillustrate significant test error reduction over unseen domains. Our secondmajor contribution consists of algorithms for computing outgoing messages fromclique clusters with symmetric clique potentials. Our algorithms are exact forarbitrary symmetric potentials on binary labels and for max-like andmajority-like potentials on multiple labels. For majority potentials, we alsoprovide an efficient Lagrangian Relaxation based algorithm that comparesfavorably with the exact algorithm. We present a 13/15-approximation algorithmfor the NP-hard Potts potential, with runtime sub-quadratic in the clique size.In contrast, the best known previous guarantee for graphs with Potts potentialsis only 1/2. We empirically show that our method for Potts potentials is anorder of magnitude faster than the best alternatives, and our LagrangianRelaxation based algorithm for majority potentials beats the best applicableheuristic -- ICM.

Generalized Collective Inference with Symmetric Clique Potentials