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Taxonomies are semantic hierarchies of concepts. One limitation of currenttaxonomy learning systems is that they define concepts as single words. Thisposition paper argues that contextualized word representations, which recentlyachieved state-of-the-art results on many competitive NLP tasks, are apromising method to address this limitation. We outline a novel approach fortaxonomy learning that (1) defines concepts as synsets, (2) learnsdensity-based approximations of contextualized word representations, and (3)can measure similarity and hypernymy among them.

Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper