@inproceedings{kurtz-etal-2020-end,
    title = "End-to-End Negation Resolution as Graph Parsing",
    author = "Kurtz, Robin  and
      Oepen, Stephan  and
      Kuhlmann, Marco",
    editor = "Bouma, Gosse  and
      Matsumoto, Yuji  and
      Oepen, Stephan  and
      Sagae, Kenji  and
      Seddah, Djam{\'e}  and
      Sun, Weiwei  and
      S{\o}gaard, Anders  and
      Tsarfaty, Reut  and
      Zeman, Dan",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.iwpt-1.3/",
    doi = "10.18653/v1/2020.iwpt-1.3",
    pages = "14--24",
    abstract = "We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representation-specific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model."
}