  Taxonomies are semantic hierarchies of concepts. One limitation of current
taxonomy learning systems is that they define concepts as single words. This
position paper argues that contextualized word representations, which recently
achieved state-of-the-art results on many competitive NLP tasks, are a
promising method to address this limitation. We outline a novel approach for
taxonomy learning that (1) defines concepts as synsets, (2) learns
density-based approximations of contextualized word representations, and (3)
can measure similarity and hypernymy among them.
