To address this problem, some recent efforts, such as (Reisinger andMooney, 2010) (Huang et al., 2012), have investigated how to obtain multi embedding vectors for the respective different prototypes of a polysemous word.
these works usually take a two-step approach: they first train single prototype word representations through a multi-layer neural network with the assumption that one word only yields single word embedding; then, they identify multi word embeddings for each polysemous word by clustering all its context window features, which are usually computed as the average of single prototype embeddings of its neighboring words in the context window.