research-article

Study on Prediction of Legal Judgments Based on the CNN-BiGRU Model

Authors:

Xiaoning JinAuthors Info & Claims

ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

Pages 63 - 68

https://doi.org/10.1145/3404555.3404573

Published: 20 August 2020 Publication History

Abstract

As the cases exploded, leading legal judgment prediction becomes a promising application of artificial intelligence techniques in the legal field. The goal of legal judgment prediction is to predict the judgment results based on the facts information of a case. However, the classifier of the traditional method has poor accuracy performance and cost large computational time. The commonly used deep learning models are CNN and RNN. In this paper, CNN-BiGRU was established and analyzed, which combined the good extraction ability of CNN for local feature information and RNN for long-term dependencies information of the text. Compared with the CAIL 2018 dataset, the prediction accuracy of the charges, law articles and the terms of penalty are 94.8%, 93.6%, and 73.4%, respectively. Results showed that CNN-BiGRU has a higher prediction accuracy than CNN or RNN alone and a good training efficiency over baselines. The effectiveness and practicability of this model are validated.

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Cited By

Pandi S SFarook AKingston WKannaiah S(2024)Enlightening Justice: Empowering Society Through AI-Driven Legal Assistance2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690793(1-7)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690793
Park MChai S(2021)AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion CasesApplied Sciences10.3390/app11231108011:23(11080)Online publication date: 23-Nov-2021
https://doi.org/10.3390/app112311080

Index Terms

Study on Prediction of Legal Judgments Based on the CNN-BiGRU Model
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

April 2020

563 pages

ISBN:9781450377089

DOI:10.1145/3404555

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Tsukuba: University of Tsukuba

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Published: 20 August 2020

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ICCAI '20

ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence

April 23 - 26, 2020

Tianjin, China

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Cited By

Pandi S SFarook AKingston WKannaiah S(2024)Enlightening Justice: Empowering Society Through AI-Driven Legal Assistance2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690793(1-7)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690793
Park MChai S(2021)AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion CasesApplied Sciences10.3390/app11231108011:23(11080)Online publication date: 23-Nov-2021
https://doi.org/10.3390/app112311080

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