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Do you want to push past the simple NLP models (logistic regression, PCFG, etc.) that we've all been using for 20 years? Then this tutorial is extremely practical for you!
Statistical natural language processing relies on probabilistic models of linguistic structure. More complex models can help capture our intuitions about language, by adding linguistically meaningful interactions and latent variables. However, inference and learning in the models we want often poses a serious computational challenge. Belief propagation (BP) and its variants provide an attractive approximate solution, especially using recent training methods. These approaches can handle joint models of interacting components, are computationally efficient, and have extended the state-of-the-art on a number of common NLP tasks, including dependency parsing, modeling of morphological paradigms, CCG parsing, phrase extraction, semantic role labeling, and information extraction (Smith and Eisner, 2008, Dreyer and Eisner, 2009, Auli and Lopez, 2011, Burkett and Klein, 2012, Naradowsky et al., 2012, Stoyanov and Eisner, 2012).
This tutorial delves into BP with an emphasis on recent advances that enable state-of-the-art performance in a variety of tasks. Our goal is to elucidate how these approaches can easily be applied to new problems. We also cover the theory underlying them. Our target audience is researchers in human language technologies; we do not assume familarity with BP. In the first three sections, we discuss applications of BP to NLP problems, the basics of modeling with factor graphs and message passing, and the theoretical underpinnings of “what BP is doing” and how it relates to other variational inference techniques. In the second three sections, we cover key extensions to the standard BP algorithm to enable modeling of linguistic structure, efficient inference, and approximation-aware training. We survey a variety of software tools and introduce a new software framework that incorporates many of the modern approaches covered in this tutorial.
The links below are for the per-section slides. Included are the extra Appendix slides for sections 2 and 3.