@inproceedings{krebs-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 10: Capturing Discriminative Attributes",
    author = "Krebs, Alicia  and
      Lenci, Alessandro  and
      Paperno, Denis",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/S18-1117/",
    doi = "10.18653/v1/S18-1117",
    pages = "732--740",
    abstract = "This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that `urine' is a discriminating feature in the word pair `kidney', `bone'. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score."
}