@inproceedings{brychcin-etal-2018-uwb,
    title = "{UWB} at {S}em{E}val-2018 Task 10: Capturing Discriminative Attributes from Word Distributions",
    author = "Brychc{\'i}n, Tom{\'a}{\v{s}}  and
      Hercig, Tom{\'a}{\v{s}}  and
      Steinberger, Josef  and
      Konkol, Michal",
    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/jlcl-multiple-ingestion/S18-1153/",
    doi = "10.18653/v1/S18-1153",
    pages = "935--939",
    abstract = "We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1{\%} and is ranked {\#}4 among 26 submitted systems."
}