@inproceedings{jain-espinosa-anke-2022-distilling,
    title = "Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction",
    author = "Jain, Devansh  and
      Espinosa Anke, Luis",
    editor = "Nastase, Vivi  and
      Pavlick, Ellie  and
      Pilehvar, Mohammad Taher  and
      Camacho-Collados, Jose  and
      Raganato, Alessandro",
    booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.starsem-1.13/",
    doi = "10.18653/v1/2022.starsem-1.13",
    pages = "151--156",
    abstract = "In this paper, we analyze zero-shot taxonomy learning methods which are based on distilling knowledge from language models via prompting and sentence scoring. We show that, despite their simplicity, these methods outperform some supervised strategies and are competitive with the current state-of-the-art under adequate conditions. We also show that statistical and linguistic properties of prompts dictate downstream performance."
}