@InProceedings{pasini-navigli:2017:EMNLP2017,
  author    = {Pasini, Tommaso  and  Navigli, Roberto},
  title     = {Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data},
  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     = {78--88},
  abstract  = {Annotating large numbers of sentences with senses is the heaviest requirement
	of current Word Sense Disambiguation. We present Train-O-Matic, a
	language-independent method for generating millions of sense-annotated training
	instances for virtually all meanings of words in a language's vocabulary. The
	approach is fully automatic: no human intervention is required and the only
	type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves
	consistently state-of-the-art performance across gold standard datasets and
	languages, while at the same time removing the burden of manual annotation. All
	the training data is available for research purposes at http://trainomatic.org.},
  url       = {https://www.aclweb.org/anthology/D17-1008}
}