  Taxonomy construction is not only a fundamental task for semantic analysis of
text corpora, but also an important step for applications such as information
filtering, recommendation, and Web search. Existing pattern-based methods
extract hypernym-hyponym term pairs and then organize these pairs into a
taxonomy. However, by considering each term as an independent concept node,
they overlook the topical proximity and the semantic correlations among terms.
In this paper, we propose a method for constructing topic taxonomies, wherein
every node represents a conceptual topic and is defined as a cluster of
semantically coherent concept terms. Our method, TaxoGen, uses term embeddings
and hierarchical clustering to construct a topic taxonomy in a recursive
fashion. To ensure the quality of the recursive process, it consists of: (1) an
adaptive spherical clustering module for allocating terms to proper levels when
splitting a coarse topic into fine-grained ones; (2) a local embedding module
for learning term embeddings that maintain strong discriminative power at
different levels of the taxonomy. Our experiments on two real datasets
demonstrate the effectiveness of TaxoGen compared with baseline methods.
