Dinu and Lapata (2010) advocate a method that resembles LVW, in that it uses a distribution over latent dimensions in order to measure semantic shifts in context.
whereas their approach computes the contextualized meaning directly within the latent space, the LVW approach we adopt in this article exploits the latent space to determine the features that are important for a particular context, and adapt the original (out-of-context) dependency-based feature vector of the target word accordingly.