5 research outputs found
Predicting the Law Area and Decisions of French Supreme Court Cases
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.Comment: RANLP 201
Arabic tweeps dialect prediction based on machine learning approach
In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector