@inproceedings{jacobs-etal-2018-economic,
    title = "Economic Event Detection in Company-Specific News Text",
    author = "Jacobs, Gilles  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    editor = "Hahn, Udo  and
      Hoste, V{\'e}ronique  and
      Tsai, Ming-Feng",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3101",
    doi = "10.18653/v1/W18-3101",
    pages = "1--10",
    abstract = "This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups",
}