@inproceedings{durlich-etal-2023-causes,
    title = "What Causes Unemployment? Unsupervised Causality Mining from {S}wedish Governmental Reports",
    author = {D{\"u}rlich, Luise  and
      Nivre, Joakim  and
      Stymne, Sara},
    editor = "Ilinykh, Nikolai  and
      Morger, Felix  and
      Dann{\'e}lls, Dana  and
      Dobnik, Simon  and
      Megyesi, Be{\'a}ta  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, the Faroe Islands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.resourceful-1.4/",
    pages = "25--29",
    abstract = "Extracting statements about causality from text documents is a challenging task in the absence of annotated training data. We create a search system for causal statements about user-specified concepts by combining pattern matching of causal connectives with semantic similarity ranking, using a language model fine-tuned for semantic textual similarity. Preliminary experiments on a small test set from Swedish governmental reports show promising results in comparison to two simple baselines."
}