  Vector space representations of words capture many aspects of word
similarity, but such methods tend to make vector spaces in which antonyms (as
well as synonyms) are close to each other. We present a new signed spectral
normalized graph cut algorithm, signed clustering, that overlays existing
thesauri upon distributionally derived vector representations of words, so that
antonym relationships between word pairs are represented by negative weights.
Our signed clustering algorithm produces clusters of words which simultaneously
capture distributional and synonym relations. We evaluate these clusters
against the SimLex-999 dataset (Hill et al.,2014) of human judgments of word
pair similarities, and also show the benefit of using our clusters to predict
the sentiment of a given text.
