Evaluating Topic Quality with Posterior Variability

IJCNLP 2019 Linzi XingMichael J. PaulGiuseppe Carenini

Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters. We derive a novel measure of LDA topic quality using the variability of the posterior distributions... (read more)

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