We demonstrate that the above construction of a word's representation disambiguated in a context is useful by comparing with state-of-the-art unsupervised methods for polysemy disambiguation on two tasks: Word Sense Induction and contextual similarity.
we compare with MSSG, the K-Grassmeans model of (Mu et al., 2017), and the sparse coding method of (Arora et al., 2018).