  Hierarchical latent tree analysis (HLTA) is recently proposed as a new method
for topic detection. It differs fundamentally from the LDA-based methods in
terms of topic definition, topic-document relationship, and learning method. It
has been shown to discover significantly more coherent topics and better topic
hierarchies. However, HLTA relies on the Expectation-Maximization (EM)
algorithm for parameter estimation and hence is not efficient enough to deal
with large datasets. In this paper, we propose a method to drastically speed up
HLTA using a technique inspired by recent advances in the moments method.
Empirical experiments show that our method greatly improves the efficiency of
HLTA. It is as efficient as the state-of-the-art LDA-based method for
hierarchical topic detection and finds substantially better topics and topic
hierarchies.
