@inproceedings{king-etal-2018-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes",
    author = "King, Milton  and
      Hakimi Parizi, Ali  and
      Cook, Paul",
    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/S18-1168/",
    doi = "10.18653/v1/S18-1168",
    pages = "1013--1016",
    abstract = "In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model."
}