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ACL
2009

Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering

10 years 9 months ago
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.
Jian Huang 0002, Sarah M. Taylor, Jonathan L. Smit
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Jian Huang 0002, Sarah M. Taylor, Jonathan L. Smith, Konstantinos A. Fotiadis, C. Lee Giles
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