  Recently, topic modeling has been widely used to discover the abstract topics
in text corpora. Most of the existing topic models are based on the assumption
of three-layer hierarchical Bayesian structure, i.e. each document is modeled
as a probability distribution over topics, and each topic is a probability
distribution over words. However, the assumption is not optimal. Intuitively,
it's more reasonable to assume that each topic is a probability distribution
over concepts, and then each concept is a probability distribution over words,
i.e. adding a latent concept layer between topic layer and word layer in
traditional three-layer assumption. In this paper, we verify the proposed
assumption by incorporating the new assumption in two representative topic
models, and obtain two novel topic models. Extensive experiments were conducted
among the proposed models and corresponding baselines, and the results show
that the proposed models significantly outperform the baselines in terms of
case study and perplexity, which means the new assumption is more reasonable
than traditional one.
