A Study on Argument-Based Analysis of Legal Model

Abstract

Unbounded delay in the delivery of justice has resulted in large number of pending cases thereby affecting the well-being of beneficiary in a broader sense. Due to this delay, complainant, accused or witness may either become unfit for trail or hostile, etc. which may jeopardize the promptness of judicial system. However, its efficiency can be enhanced using Machine Learning algorithm thereby reducing the workload of legal professional so that they can engage more time to resolve those pending cases. This paper focuses on argument-based prediction system over legal data set. Precisely, we have applied Supervised Machine Learning technique over the cases related to 'Domestic Violence Against Women' and have proposed a model to predict guilt of an accused. We have approached in sequential manner, precisely: (i) Hard-copies of argument based legal documents are collected from various trial courts of West Bengal. (ii) Data set are generated manually based on specific parameters which act as deciding factors for prediction of accusation. (iii) Supervised Machine Learning algorithm are used to train those legal data set to predict our desired output. We have validated the performance and accuracy of our proposed legal prediction system using standard classifiers like Decision Tree, Naive-Bayes Classifier and K-Nearest Neighbor (KNN).

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

“…Work [ 21 ] uses convolutional neural networks (CNNs) to solve the problem of predicting the European Court of Human Rights (ECHR) judgments automatically by pretraining and customizing the textual representations considering word embeddings and statistically testing them to gather sufficient statistical evidence. Work [ 22 ] applies supervised machine learning model to cases about the “domestic violence for women” and proposes a model for predicting the guilt of the accused. Experiments have shown that the performance and accuracy of legal prediction systems can reduce the workload of legal professionals.…”