Uncertainty in Artificial Intelligence
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The Author-Topic Model for Authors and Documents
Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors. We apply the model to a collection of 1,700 NIPS conference papers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative models for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each author is associated with a distribution over words rather than a distribution over topics. We show topics recovered by the author-topic model, and demonstrate applications to computing similarity between authors and entropy of author output.
Keywords: null
Pages: 487-494
PS Link: http://www.datalab.uci.edu/author-topic/
PDF Link: /papers/04/p487-rosen-zvi.pdf
AUTHOR = "Michal Rosen-Zvi and Thomas Griffiths and Mark Steyvers and Padhraic Smyth",
TITLE = "The Author-Topic Model for Authors and Documents",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "487--494"

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