@InProceedings{glavavs-ponzetto:2017:EMNLP2017,
  author    = {Glava\v{s}, Goran  and  Ponzetto, Simone Paolo},
  title     = {Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1757--1767},
  abstract  = {Detection of lexico-semantic relations is one of the central tasks of
	computational semantics. Although some fundamental relations (e.g., hypernymy)
	are asymmetric, most existing models account for asymmetry only implicitly and
	use the same concept representations to support detection of symmetric and
	asymmetric relations alike. In this work, we propose the Dual Tensor model, a
	neural architecture with which we explicitly model the asymmetry and capture
	the translation between unspecialized and specialized word embeddings via a
	pair of tensors. Although our Dual Tensor model needs only unspecialized
	embeddings as input, our experiments on hypernymy and meronymy detection
	suggest that it can outperform more complex and resource-intensive models. We
	further demonstrate that the model can account for polysemy and that it
	exhibits stable performance across languages.},
  url       = {https://www.aclweb.org/anthology/D17-1185}
}