@inproceedings{surdeanu-etal-2022-taxonomy,
    title = "Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction",
    author = "Surdeanu, Mihai  and
      Hungerford, John  and
      Chan, Yee Seng  and
      MacBride, Jessica  and
      Gyori, Benjamin  and
      Zupon, Andrew  and
      Tang, Zheng  and
      Qiu, Haoling  and
      Min, Bonan  and
      Zverev, Yan  and
      Hilverman, Caitlin  and
      Thomas, Max  and
      Andrews, Walter  and
      Alcock, Keith  and
      Zhang, Zeyu  and
      Reynolds, Michael  and
      Bethard, Steven  and
      Sharp, Rebecca  and
      Laparra, Egoitz",
    booktitle = "Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.hcinlp-1.1",
    doi = "10.18653/v1/2022.hcinlp-1.1",
    pages = "1--10",
    abstract = "An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none.When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one which does not scale well.Here we propose an interactive tool that allows a taxonomy to be built or extended \textit{rapidly} and with a \textit{human in the loop} to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour, to quickly build or extend a taxonomy to better fit information needs.",
}