Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering

Coreferencing entities across documents in a large corpus enables advanced document understanding tasks such as question answering. This paper presents a novel cross document coreference approach that leverages the profiles of entities which are constructed by using information extraction tools and reconciled by using a within-document coreference module. We propose to match the profiles by using a learned ensemble distance function comprised of a suite of similarity specialists. We develop a kernelized soft relational clustering algorithm that makes use of the learned distance function to partition the entities into fuzzy sets of identities. We compare the kernelized clustering method with a popular fuzzy relation clustering algorithm (FRC) and show 5% improvement in coreference performance. Evaluation of our proposed methods on a large benchmark disambiguation collection shows that they compare favorably with the top runs in the SemEval evaluation.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Jian Huang 0002, Sarah M. Taylor, Jonathan L. Smith, Konstantinos A. Fotiadis, C. Lee Giles
• Keywords: ACL 2009, Computational Linguistics, Distance Function, Entities, Relational Clustering Algorithm