A Novel Approach on Argument based Legal Prediction Model using Machine Learning

Sil, Riya; Roy, Abhishek

doi:10.1109/icosec49089.2020.9215310

Cited by 18 publications

(5 citation statements)

References 28 publications

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“…Work [ 18 ] finds accuracy by using a support vector machine (SVM) algorithm to solve the large number of cases remaining in the Indian judicial system each year, with a focus on “dowry death” related cases, predicting justice based on the analysis of judicial arguments to achieve justice. Work [ 19 ] uses transformers' bidirectional encoder (BERT), applied to Legal Judgment Prediction and violation prediction.…”

Section: Related Workmentioning

confidence: 99%

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

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

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Section: Related Workmentioning

confidence: 99%

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

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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“…Among the highlighted cases are disengagement, divorce, parental rights and dowry. Ben-David [61] conducted a crucial study regarding court decisions in 'favour' or 'against' the termination of parental rights that found the balance between the child's best interest, the parent's right Int J Artif Intell ISSN: 2252-8938  and the privacy of the family unit [62]. Li et.…”

Section: Types Of Judicial Decisionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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<span>Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.</span>

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“…In this paper authors have performed 'Argument Based Legal Judgment Prediction' [26] related to 'Violence Against Women' cases to predict guilt of accused person [27,28]. The average performance and accuracy are compared with standard classifiers namely, Decision Tree, Naive-Bayes Classifier and K-Nearest neighbor (KNN) and the best algorithm for prediction of related cases are found.…”

Section: Discussionmentioning

confidence: 99%

A Study on Argument-Based Analysis of Legal Model

Sil

Saha

Roy

2021

Advances in Intelligent Systems and Computing

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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).

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A Novel Approach on Argument based Legal Prediction Model using Machine Learning

Cited by 18 publications

References 28 publications

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

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

A systematic literature review of machine learning methods in predicting court decisions

A Study on Argument-Based Analysis of Legal Model

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