This allows us to discover word senses in an unsupervised manner, while preserving the simplicity of the skip-gram approach.
unlike previous probabilistic models for multisense word embeddings, disambiguated skip-gram can be seen as a simple feed-forward neural network amendable to end-to-end training with back-propagation.