@inproceedings{pasini-navigli-2017-train,
    title = "Train-{O}-{M}atic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data",
    author = "Pasini, Tommaso  and
      Navigli, Roberto",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/D17-1008/",
    doi = "10.18653/v1/D17-1008",
    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 \url{http://trainomatic.org}."
}