@InProceedings{sedoc-EtAl:2017:Long,
  author    = {Sedoc, Joao  and  Gallier, Jean  and  Foster, Dean  and  Ungar, Lyle},
  title     = {Semantic Word Clusters Using Signed Spectral Clustering},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {939--949},
  abstract  = {Vector space representations of words capture many aspects of word similarity,
	but such methods tend to produce vector spaces in which antonyms (as well as
	synonyms) are close to each other. For spectral clustering using such word
	embeddings, words are points in a vector space where synonyms are linked with
	positive weights, while antonyms are linked with negative weights. We present a
	new signed spectral normalized graph cut algorithm, {\em 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 that simultaneously capture distributional and synonym
	relations.  
	By using randomized spectral decomposition (Halko et al., 2011) and sparse
	matrices, our method is both fast and scalable. We validate our clusters using
	datasets containing human judgments of word pair similarities and show the
	benefit of using our word clusters for sentiment prediction.},
  url       = {http://aclweb.org/anthology/P17-1087}
}