Entity Mention Coreference Resolution Algorithm

See: Entity Mention, Mention Coreference Resolution Algorithm, Entity Mention Coreference Resolution Task.

References

(Yang et al., 2008) ⇒ Xiaofeng Yang, Jian Su, Jun Lang, Chew Lim Tan, Ting Liu, and Sheng Li. (2008). “An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming.” In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL 2008).
- ABSTRACT: 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.