Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on case description. Several researchers have developed techniques to assist potential clients by predicting the outcome in the legal profession. However, none of the proposed techniques were implemented in Arabic, and only a few attempts were implemented in English, Chinese, and Hindi. In this paper, we develop a system that utilizes deep learning (DL) and natural language processing (NLP) techniques to predict the judgment outcome from Arabic case scripts, especially in cases of custody and annulment of marriage. This system will assist judges and attorneys in improving their work and time efficiency while reducing sentencing disparity. In addition, it will help litigants, lawyers, and law students analyze the probable outcomes of any given case before trial. We use a different machine and deep learning models such as Support Vector Machine (SVM), Logistic regression (LR), Long Short Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) using representation techniques such as TF-IDF and word2vec on the developed dataset. Experimental results demonstrate that compared with the five baseline methods, the SVM model with word2vec and LR with TF-IDF achieve the highest accuracy of 88% and 78% in predicting the judgment on custody cases and annulment of marriage, respectively. Furthermore, the LR and SVM with word2vec and BiLSTM model with TF-IDF achieved the highest accuracy of 88% and 69% in predicting the probability of outcomes on custody cases and annulment of marriage, respectively.

翻译：法律判决预测（Legal Judgment Prediction, LJP）旨在根据案情描述预测判决结果。已有研究人员开发多项技术，通过预测法律领域判决结果来辅助潜在客户。然而，现有技术均未在阿拉伯语环境下实现，仅少数尝试涉及英语、中文和印地语。本文开发了一套系统，利用深度学习（DL）与自然语言处理（NLP）技术，针对阿拉伯语案件文本（尤其是监护权与婚姻无效案件）预测判决结果。该系统将帮助法官和律师提高工作效率与时间利用率，同时减少量刑差异。此外，它还能协助诉讼当事人、律师及法学院学生在庭审前分析任何给定案件的潜在结果。我们在自建数据集上采用支持向量机（SVM）、逻辑回归（LR）、长短期记忆网络（LSTM）和双向长短期记忆网络（BiLSTM）等机器学习与深度学习模型，并运用TF-IDF与word2vec等表示技术。实验结果表明，与五种基线方法相比，基于word2vec的SVM模型和基于TF-IDF的LR模型在监护权案件与婚姻无效案件的判决预测中分别达到88%和78%的最高准确率。此外，基于word2vec的LR与SVM模型以及基于TF-IDF的BiLSTM模型在监护权案件与婚姻无效案件的结果概率预测中分别达到88%和69%的最高准确率。