@InProceedings{jacobs-lefever-hoste:2018:W18-31,
  author    = {Jacobs, Gilles  and  Lefever, Els  and  Hoste, Véronique},
  title     = {Economic Event Detection in Company-Specific News Text},
  booktitle = {Proceedings of the First Workshop on Economics and Natural Language Processing},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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},
  url       = {http://www.aclweb.org/anthology/W18-3101}
}