  In this paper, we investigate whether an a priori disambiguation of word
senses is strictly necessary or whether the meaning of a word in context can be
disambiguated through composition alone. We evaluate the performance of
off-the-shelf single-vector and multi-sense vector models on a benchmark phrase
similarity task and a novel task for word-sense discrimination. We find that
single-sense vector models perform as well or better than multi-sense vector
models despite arguably less clean elementary representations. Our findings
furthermore show that simple composition functions such as pointwise addition
are able to recover sense specific information from a single-sense vector model
remarkably well.
