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

The traditional mention-pair model for coreference 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 performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming (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 coreference decision. The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the coreference resolution task.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Xiaofeng Yang, Jian Su, Jun Lang, Chew Lim Tan, Ting Liu, Sheng Li
• Keywords: ACL 2008, Computational Linguistics, Coreference Resolution, Entity-mention Model, Model