@inproceedings{maru-etal-2019-syntagnet,
    title = "{S}yntag{N}et: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations",
    author = "Maru, Marco  and
      Scozzafava, Federico  and
      Martelli, Federico  and
      Navigli, Roberto",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/D19-1359/",
    doi = "10.18653/v1/D19-1359",
    pages = "3534--3540",
    abstract = "Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. This paper introduces SyntagNet, a novel resource consisting of manually disambiguated lexical-semantic combinations. By capturing sense distinctions evoked by syntagmatic relations, SyntagNet enables knowledge-based WSD systems to establish a new state of the art which challenges the hitherto unrivaled performances attained by supervised approaches. To the best of our knowledge, SyntagNet is the first large-scale manually-curated resource of this kind made available to the community (at \url{http://syntagnet.org})."
}