Classic distributional semantics attempted to do composition to account for contextual effects, but it was in general unable to go beyond short phrases (Baroni, 2013); newer-generation neural network models have supposed a big step forward, as they can natively do composition (Westera and Boleda, 2019).
the hidden layer activations in an RNN can be seen as putting words in context, as they combine the word embedding with information coming from the context (the adjacent hidden states).