  As the emergence and the thriving development of social networks, a huge
number of short texts are accumulated and need to be processed. Inferring
latent topics of collected short texts is useful for understanding its hidden
structure and predicting new contents. Unlike conventional topic models such as
latent Dirichlet allocation (LDA), a biterm topic model (BTM) was recently
proposed for short texts to overcome the sparseness of document-level word
co-occurrences by directly modeling the generation process of word pairs.
Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and
collapsed variational inference have been proposed for BTM. However, they
either require large computational complexity, or rely on very crude
estimation. In this work, we develop a stochastic divergence minimization
inference algorithm for BTM to estimate latent topics more accurately in a
scalable way. Experiments demonstrate the superiority of our proposed algorithm
compared with existing inference algorithms.
