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

TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters