@InProceedings{ferret:2017:I17-1,
  author    = {Ferret, Olivier},
  title     = {Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {273--283},
  abstract  = {In this article, we propose to investigate a new problem consisting in turning
	a distributional thesaurus into dense word vectors. We propose more precisely a
	method for performing such task by associating graph embedding and  distributed
	representation adaptation. We have applied and evaluated it for English nouns
	at a large scale about its ability to retrieve synonyms. In this context, we
	have also illustrated the interest of the developed method for three different
	tasks: the improvement of already existing word embeddings, the fusion of
	heterogeneous representations and the expansion of synsets.},
  url       = {http://www.aclweb.org/anthology/I17-1028}
}