ALJP：基于机器学习模型的阿拉伯语个人身份案件法律判决预测
ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models
AI总结：
本文提出一个基于机器学习和深度学习模型的阿拉伯语法律判决预测系统，在监护权和婚姻无效案件中分别达到88%和78%的最高准确率，旨在辅助法官、律师和诉讼当事人。
摘要：
法律判决预测（LJP）旨在根据案件描述预测判决结果。已有若干研究者开发了技术来协助潜在客户在法律行业中预测结果。然而，所提出的技术均未在阿拉伯语中实施，仅有少数尝试在英语、中文和印地语中实施。在本文中，我们开发了一个系统，利用深度学习（DL）和自然语言处理（NLP）技术，从阿拉伯语案件文本中预测判决结果，特别是在监护权和婚姻无效案件中。该系统将协助法官和律师提高工作效率和时间效率，同时减少量刑差异。此外，它将帮助诉讼当事人、律师和法律专业学生在审判前分析任何给定案件的可能结果。我们在所开发的数据集上使用了不同的机器学习和深度学习模型，如支持向量机（SVM）、逻辑回归（LR）、长短期记忆（LSTM）和双向长短期记忆（BiLSTM），并使用TF-IDF和word2vec等表示技术。实验结果表明，与五种基线方法相比，使用word2vec的SVM模型和使用TF-IDF的LR模型在预测监护权案件和婚姻无效案件的判决上分别达到了88%和78%的最高准确率。此外，使用word2vec的LR和SVM以及使用TF-IDF的BiLSTM模型在预测监护权案件和婚姻无效案件的结果概率上分别达到了88%和69%的最高准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
