@InProceedings{jameel-bouraoui-schockaert:2018:Long,
  author    = {Jameel, Shoaib  and  Bouraoui, Zied  and  Schockaert, Steven},
  title     = {Unsupervised Learning of Distributional Relation Vectors},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {23--33},
  abstract  = {Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space.},
  url       = {http://www.aclweb.org/anthology/P18-1003}
}