@inproceedings{ein-dor-etal-2019-financial,
    title = "Financial Event Extraction Using {W}ikipedia-Based Weak Supervision",
    author = "Ein-Dor, Liat  and
      Gera, Ariel  and
      Toledo-Ronen, Orith  and
      Halfon, Alon  and
      Sznajder, Benjamin  and
      Dankin, Lena  and
      Bilu, Yonatan  and
      Katz, Yoav  and
      Slonim, Noam",
    editor = "Hahn, Udo  and
      Hoste, V{\'e}ronique  and
      Zhang, Zhu",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5102",
    doi = "10.18653/v1/D19-5102",
    pages = "10--15",
    abstract = "Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.",
}