The Grouped Author-Topic Model for Unsupervised Entity Resolution
Andrew Dai and Amos Storkey
In: 21st International Conference on Artificial Neural Networks, 14-17 Jun 2011, Espoo, Finland.
This paper describes a generative approach for tackling the problem of identity resolution in a completely unsupervised context with no fixed assumption regarding the true number of identities. The problem of entity resolution involves associating different references to authors (in a paper’s author list, for example) with real underlying identities. The references may be written in differing forms or may have errors, and identical references may refer to different real identities. The approach taken here uses a generative model of both the abstract of a document and its list of authors to resolve identities in a corpus of documents. In the model, authors and topics are associated with latent groups. For each document, an abstract and an author list are generated conditioned on a given group. Results are presented on real-world datasets, and outperform the best performing unsupervised methods.