  Automated generation of high-quality topical hierarchies for a text
collection is a dream problem in knowledge engineering with many valuable
applications. In this paper a scalable and robust algorithm is proposed for
constructing a hierarchy of topics from a text collection. We divide and
conquer the problem using a top-down recursive framework, based on a tensor
orthogonal decomposition technique. We solve a critical challenge to perform
scalable inference for our newly designed hierarchical topic model. Experiments
with various real-world datasets illustrate its ability to generate robust,
high-quality hierarchies efficiently. Our method reduces the time of
construction by several orders of magnitude, and its robust feature renders it
possible for users to interactively revise the hierarchy.
