@InProceedings{konkol:2017:RANLP,
  author    = {Konkol, Michal},
  title     = {Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {394--400},
  abstract  = {In this paper, we introduce WoRel, a model that jointly learns word embeddings
	and a semantic representation of word relations. The model learns from plain
	text sentences and their dependency parse trees. The word embeddings produced
	by WoRel outperform Skip-Gram and GloVe in word similarity and syntactical word
	analogy tasks and have comparable results on word relatedness and semantic word
	analogy tasks. We show that the semantic representation of relations enables us
	to express the meaning of phrases and is a promising research direction for
	semantics at the sentence level.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_052}
}