  We propose a Bayesian generative model for incorporating prior domain
knowledge into hierarchical topic modeling. Although embedded topic models
(ETMs) and its variants have gained promising performance in text analysis,
they mainly focus on mining word co-occurrence patterns, ignoring potentially
easy-to-obtain prior topic hierarchies that could help enhance topic coherence.
While several knowledge-based topic models have recently been proposed, they
are either only applicable to shallow hierarchies or sensitive to the quality
of the provided prior knowledge. To this end, we develop a novel deep ETM that
jointly models the documents and the given prior knowledge by embedding the
words and topics into the same space. Guided by the provided knowledge, the
proposed model tends to discover topic hierarchies that are organized into
interpretable taxonomies. Besides, with a technique for adapting a given graph,
our extended version allows the provided prior topic structure to be finetuned
to match the target corpus. Extensive experiments show that our proposed model
efficiently integrates the prior knowledge and improves both hierarchical topic
discovery and document representation.
