We present a generative model of template-filling in which coreference resolution and role assignment are jointly determined. Underlying template roles first generate abstract entities, which in turn generate concrete textual mentions. On the standard corporate acquisitions dataset, joint resolution in our entity-level model reduces error over a mention-level discriminative approach by up to 20%.
Recently, REF presented a generative semi-supervised approach for template filling.
An Entity-Level Approach to Information Extraction
Proceedings of the ACL 2010 Conference Short Papers
Computer Science