Paper: An Entity-Level Approach to Information Extraction

ACL ID P10-2054
Title An Entity-Level Approach to Information Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

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%.