Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models


James Foulds, Shachi Kumar, Lise Getoor ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:777-786, 2015.


Topic models have become increasingly prominent text-analytic machine learning tools for research in the social sciences and the humanities. In particular, custom topic models can be developed to answer specific research questions. The design of these models requires a non-trivial amount of effort and expertise, motivating general-purpose topic modeling frameworks. In this paper we introduce latent topic networks, a flexible class of richly structured topic models designed to facilitate applied research. Custom models can straightforwardly be developed in our framework with an intuitive first-order logical probabilistic programming language. Latent topic networks admit scalable training via a parallelizable EM algorithm which leverages ADMM in the M-step. We demonstrate the broad applicability of the models with case studies on modeling influence in citation networks, and U.S. Presidential State of the Union addresses.

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