Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling

12 Jan 2017 Angela Fan Finale Doshi-Velez Luke Miratrix

Latent Dirichlet Allocation (LDA) models trained without stopword removal often produce topics with high posterior probabilities on uninformative words, obscuring the underlying corpus content. Even when canonical stopwords are manually removed, uninformative words common in that corpus will still dominate the most probable words in a topic... (read more)

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