Christoph Carl Kling
ABSTRACT
In this tutorial, we teach the intuition and the assumptions behind topic models. Topic models explain the co-occurrences of words in documents by extracting sets of semantically related words, called topics. These topics are semantically coherent and can be interpreted by humans. Starting with the most popular topic model, Latent Dirichlet Allocation (LDA), we explain the fundamental concepts of probabilistic topic modeling. We organise our tutorial as follows: After a general introduction, we will enable participants to develop an intuition for the underlying concepts of probabilistic topic models. Building on this intuition, we cover the technical foundations of topic models, including graphical models and Gibbs sampling. We conclude the tutorial with an overview on the most relevant adaptions and extensions of LDA.
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993--1022, 2003. Google Scholar
Digital Library
- J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, and D. M. Blei. Reading tea leaves: How humans interpret topic models. In NIPS, pages 288--296, 2009.Google ScholarDigital Library
- L. Dietz, S. Bickel, and T. Scheffer. Unsupervised prediction of citation influences. In ICML, pages 233--240. ACM, 2007. Google ScholarDigital Library
Index Terms
- Topic model tutorial: A basic introduction on latent dirichlet allocation and extensions for web scientists
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