# Evaluating Topic Quality with Posterior Variability

arXiv: https://arxiv.org/abs/1909.03524 · HTML: https://mlsanity.com/paper/1909.03524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Diversity-Aware Coherence Loss for Improving Neural Topic Models](https://mlsanity.com/paper/2305.16199/) (2023)

## Abstract (arXiv)

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. Compared to several existing baselines for automatic topic evaluation, the proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. We additionally demonstrate that topic quality estimation can be further improved using a supervised estimator that combines multiple metrics.