@InProceedings{csulea-EtAl:2017:RANLP,
  author    = {\c{S}ulea, Octavia-Maria  and  Zampieri, Marcos  and  Vela, Mihaela  and  van Genabith, Josef},
  title     = {Predicting the Law Area and Decisions of French Supreme Court Cases},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {716--722},
  abstract  = {In this paper, we investigate the application of text classification methods to
	predict the law area and the decision of cases judged by the French Supreme
	Court. We also investigate the influence of the time period in which a ruling
	was made over the textual form of the case description and the extent to which
	it is necessary to mask the judge's motivation for a ruling to emulate a
	real-world test scenario. We report results of 96% f1 score in predicting a
	case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1
	score in estimating the time span when a ruling has been issued using a linear
	Support Vector Machine (SVM) classifier trained on lexical features.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_092}
}