Paper: An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming

ACL ID P08-1096
Title An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

The traditional mention-pair model for coref- erence resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that per- forms coreference resolution at an entity level. The model adopts the Inductive Logic Pro- gramming (ILP) algorithm, which provides a relational way to organize different knowledge of entities and mentions. The solution can explicitly express relations between an entity and the contained mentions, and automatically learn first-order rules important for corefer- ence decision. The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the coreference resolu- tion task.