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Taxonomy construction is not only a fundamental task for semantic analysis oftext corpora, but also an important step for applications such as informationfiltering, recommendation, and Web search. Existing pattern-based methodsextract hypernym-hyponym term pairs and then organize these pairs into ataxonomy. 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, whereinevery node represents a conceptual topic and is defined as a cluster ofsemantically coherent concept terms. Our method, TaxoGen, uses term embeddingsand hierarchical clustering to construct a topic taxonomy in a recursivefashion. To ensure the quality of the recursive process, it consists of: (1) anadaptive spherical clustering module for allocating terms to proper levels whensplitting a coarse topic into fine-grained ones; (2) a local embedding modulefor learning term embeddings that maintain strong discriminative power atdifferent levels of the taxonomy. Our experiments on two real datasetsdemonstrate the effectiveness of TaxoGen compared with baseline methods.

TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering