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|Title:||An entity-mention model for coreference resolution with inductive logic programming|
|Citation:||Yang, X.,Su, J.,Lang, J.,Tan, C.L.,Liu, T.,Li, S. (2008). An entity-mention model for coreference resolution with inductive logic programming. ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference : 843-851. ScholarBank@NUS Repository.|
|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 relationalway to organize different knowledge of entities and mentions. The solution can explicitly express relations between an entity and the containedmentions, 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. © 2008 Association for Computational Linguistics.|
|Source Title:||ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference|
|Appears in Collections:||Staff Publications|
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