@inproceedings{qian-etal-2020-learning,
    title = "Learning Structured Representations of Entity Names using {A}ctive {L}earning and Weak Supervision",
    author = "Qian, Kun  and
      Chozhiyath Raman, Poornima  and
      Li, Yunyao  and
      Popa, Lucian",
    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.517/",
    doi = "10.18653/v1/2020.emnlp-main.517",
    pages = "6376--6383",
    abstract = "Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples."
}