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.
Yang et al. REF devised an entity-mention model that applies inductive logic programming to represent the relational knowledge of an active mention, an entity, and the mentions in the entity.
An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming
Annual Meeting Of The Association For Computational Linguistics
Computer Science