@InProceedings{nikolentzos-EtAl:2017:EACLshort,
  author    = {Nikolentzos, Giannis  and  Meladianos, Polykarpos  and  Rousseau, Francois  and  Stavrakas, Yannis  and  Vazirgiannis, Michalis},
  title     = {Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {450--455},
  abstract  = {Recently, there has been a lot of activity in learning distributed
	representations of words in vector spaces. Although there are models capable of
	learning high-quality distributed representations of words, how to generate
	vector representations of the same quality for phrases or documents still
	remains a challenge. In this paper, we propose to model each document as a
	multivariate Gaussian distribution based on the distributed representations of
	its words. We then measure the similarity between two documents based on the
	similarity of their distributions. Experiments on eight standard text
	categorization datasets demonstrate the effectiveness of the proposed approach
	in comparison with state-of-the-art methods.},
  url       = {http://www.aclweb.org/anthology/E17-2072}
}