A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean method and empirical likelihood method. In this paper, we demonstrate experimentally that commonly-used methods are unlikely to accurately estimate the probability of heldout documents, and propose two alternative methods that are both accurate and efficient.
The early work on evaluating topic models calculated the likelihood of held-out documents REF .
Evaluation methods for topic models
ICML '09
Computer Science