Joint inference of entities, relations, and coreference

Although joint inference is an effective approach to avoid cascad-ing of errors when inferring multiple natural language tasks, its application to information extraction has been limited to modeling only two tasks at a time, leading to modest improvements. In this paper, we focus on the three crucial tasks of automated extraction pipelines: entity tagging, relation extraction, and coreference. We propose a single, joint graphical model that represents the various dependencies between the tasks, allowing flow of uncertainty across task boundaries. Since the resulting model has a high tree-width and contains a large number of variables, we present a novel extension to belief propagation that sparsifies the domains of variables during in-ference. Experimental results show that our joint model consistently improves results on all three tasks as we represent more dependen-cies. In particular, our joint model obtains 12 % error reduction on tagging over the isolated models