Classification of Moroccan Legal and Legislative Texts Using Machine Learning Models

Abstract

Artificial intelligence tools have revolutionized many fields, bringing significant progress in automating tasks and solving complex problems. In this article, we focus on the legal domain, where the data to be processed are specific and in large quantities. Our study consists in carrying out an automatic classification of Moroccan legal and legislative texts in Arabic. In addition, we will conduct a series of experiments to evaluate the impact of stemming, class imbalance and the impact of data quantity on the performance of the models used. Given the specificity of the Arabic language, we used Natural Language Processing (NLP) tools adapted to this language. For classification, we worked with the following models: Support Vector Machine (SVM), Random Forests (RF), K Nearest Neighbors (KNN) and Naive Bayes (NB). The results obtained are very impressive, and the comparison of model outputs enriches the debate on specificities of each model.