@InProceedings{asaadi-rudolph:2017:RepL4NLP,
  author    = {Asaadi, Shima  and  Rudolph, Sebastian},
  title     = {Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {178--185},
  abstract  = {Learning word representations to capture the semantics and compositionality of
	language has received much research interest in natural language processing.
	Beyond the popular vector space models, matrix representations for words have
	been proposed, since then, matrix multiplication can serve as natural
	composition operation. In this work, we investigate the problem of learning
	matrix representations of words. We present a learning approach for
	compositional matrix-space models for the task of sentiment analysis. We show
	that our approach, which learns the matrices gradually in two steps,
	outperforms other approaches and a gradient-descent baseline in terms of
	quality and computational cost.},
  url       = {http://www.aclweb.org/anthology/W17-2621}
}