@inproceedings{barkan-etal-2020-within,
    title = "Within-Between Lexical Relation Classification",
    author = "Barkan, Oren  and
      Caciularu, Avi  and
      Dagan, Ido",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.emnlp-main.284/",
    doi = "10.18653/v1/2020.emnlp-main.284",
    pages = "3521--3527",
    abstract = "We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation. We show that the proposed model is competitive and outperforms other baselines, across various benchmarks."
}