  Topic taxonomies, which represent the latent topic (or category) structure of
document collections, provide valuable knowledge of contents in many
applications such as web search and information filtering. Recently, several
unsupervised methods have been developed to automatically construct the topic
taxonomy from a text corpus, but it is challenging to generate the desired
taxonomy without any prior knowledge. In this paper, we study how to leverage
the partial (or incomplete) information about the topic structure as guidance
to find out the complete topic taxonomy. We propose a novel framework for topic
taxonomy completion, named TaxoCom, which recursively expands the topic
taxonomy by discovering novel sub-topic clusters of terms and documents. To
effectively identify novel topics within a hierarchical topic structure,
TaxoCom devises its embedding and clustering techniques to be closely-linked
with each other: (i) locally discriminative embedding optimizes the text
embedding space to be discriminative among known (i.e., given) sub-topics, and
(ii) novelty adaptive clustering assigns terms into either one of the known
sub-topics or novel sub-topics. Our comprehensive experiments on two real-world
datasets demonstrate that TaxoCom not only generates the high-quality topic
taxonomy in terms of term coherency and topic coverage but also outperforms all
other baselines for a downstream task.
