Cross-Document Coreference Resolution Using Latent Features

Over the last years, entity detection approaches which combine named entity recognition and entity linking have been used to detect mentions of RDF resources from a given reference knowledge base in unstructured data. In this paper, we address the problem of assigning a single URI to named entities which stand for the same real-object across documents but are not yet available in the reference knowledge base. This task is known as cross-document co-reference resolution and has been addressed by manifold approaches in the past. We present a preliminary study of a novel take on the task based on the use of latent features derived from matrix factorizations combined with parameter-free graph clustering. We study the influence of di↵erent parameters (window size, rank, hardening) on our approach by comparing the F-measures we achieve on the N benchmark. Our results suggest that using latent features leads to higher F-measures with an increase of up to 20.5% on datasets of the N collection.