  Word embeddings predict a word from its neighbours by learning small, dense
embedding vectors. In practice, this prediction corresponds to a semantic score
given to the predicted word (or term weight). We present a novel model that,
given a target word, redistributes part of that word's weight (that has been
computed with word embeddings) across words occurring in similar contexts as
the target word. Thus, our model aims to simulate how semantic meaning is
shared by words occurring in similar contexts, which is incorporated into
bag-of-words document representations. Experimental evaluation in an
unsupervised setting against 8 state of the art baselines shows that our model
yields the best micro and macro F1 scores across datasets of increasing
difficulty.
