  We present a novel method for hierarchical topic detection where topics are
obtained by clustering documents in multiple ways. Specifically, we model
document collections using a class of graphical models called hierarchical
latent tree models (HLTMs). The variables at the bottom level of an HLTM are
observed binary variables that represent the presence/absence of words in a
document. The variables at other levels are binary latent variables, with those
at the lowest latent level representing word co-occurrence patterns and those
at higher levels representing co-occurrence of patterns at the level below.
Each latent variable gives a soft partition of the documents, and document
clusters in the partitions are interpreted as topics. Latent variables at high
levels of the hierarchy capture long-range word co-occurrence patterns and
hence give thematically more general topics, while those at low levels of the
hierarchy capture short-range word co-occurrence patterns and give thematically
more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a
document generation process and use word variables instead of token variables.
They use a tree structure to model the relationships between topics and words,
which is conducive to the discovery of meaningful topics and topic hierarchies.
