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Raf Alvarado: How to Read a Topic Model

Since the invention of LDA* in 2002 (Blei, Ng, and Jordan), topic models have become a mainstay of digital humanities and text analytic research. However, the use of topic models remains narrowly conceived, and the method continues to raise epistemological concerns. In this presentation, Prof. Alvarado will demonstrate an interactive topic model browser designed to address one of these concerns, the problem of topic reference.  Do topics refer to anything in the world, such as collective representations, or do they merely provide a convenient means of dimensionality reduction for users of large text databases? Drawing from various corpora, including online newspapers of various nationalist movements, he will present some conclusions on how to make sense of topics and how we might integrate these models into scholarly arguments.

*In natural language processing, latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

Sponsoring Organization(s):

November 3, 2017 1:30 pm to 2:30 pm

Alderman Library 317 (west wing)

Event type: Lecture