ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using
  Machine Learning Models
Salwa Abbara Mona Hafez Aya Kazzaz Areej Alhothali Alhanouf Alsolami
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.