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.
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