Predicting the Law Area and Decisions of French Supreme Court Cases

Octavia-Maria Şulea, Marcos Zampieri, Mihaela Vela, Josef van Genabith


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.
Anthology ID:
R17-1092
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
716–722
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_092
DOI:
10.26615/978-954-452-049-6_092
Bibkey:
Cite (ACL):
Octavia-Maria Şulea, Marcos Zampieri, Mihaela Vela, and Josef van Genabith. 2017. Predicting the Law Area and Decisions of French Supreme Court Cases. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 716–722, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Predicting the Law Area and Decisions of French Supreme Court Cases (Şulea et al., RANLP 2017)
Copy Citation:
PDF:
https://doi.org/10.26615/978-954-452-049-6_092