Unsupervised Coreference of Publication Venues

Information about the venues of research papers is useful for information retrieval and for automatic mining of the literature. Important to processing venue information is venue coreference, the task of determining which possibly dissimilar mentions of venues refer to the same underlying venue. A natural unsupervised technique for this problem is generative mixture modeling, and indeed such models have been successfully applied to paper and author coreference. But standard models perform poorly on venue strings, because venue strings exhibit greater variance than title or author strings. In this paper, we exploit the fact that venues have characteristic distributions over titles. We do this using a generative model that explicitly models a venue-specific distribution over title words. The model uses a single set of latent variables to control two disparate clustering models: a Dirichlet-multinomial model over titles, and a nonexchangeable string-edit model over venues. Incorporating title information yields a substantial improvement in performance—a 58% reduction in error over a standard Dirichlet process mixture. The model successfully disambiguates several venues that have string-identical abbreviations.