@inproceedings{hartmann-sogaard-2021-multilingual,
    title = "Multilingual Negation Scope Resolution for Clinical Text",
    author = "Hartmann, Mareike  and
      S{\o}gaard, Anders",
    editor = "Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis",
    month = apr,
    year = "2021",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.louhi-1.2",
    pages = "7--18",
    abstract = "Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases.",
}