@inproceedings{roller-etal-2018-hearst,
    title = "Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora",
    author = "Roller, Stephen  and
      Kiela, Douwe  and
      Nickel, Maximilian",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P18-2057/",
    doi = "10.18653/v1/P18-2057",
    pages = "358--363",
    abstract = "Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods."
}