Analysis on Hybrid Deep Neural Networks for Legal Domain Multitasks: Categorization, Extraction, and Prediction

An extensive quantity of online statistics accessible in the legal domain has made legal data processing the main sector of research development. A broad variety of problems, including legal document categorization, information extraction, and prediction have been put into a scope of legitimate syst...

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Bibliographic Details
Published inInternational journal of e-collaboration Vol. 18; no. 1; pp. 1 - 22
Main Authors Vaissnave V, Deepalakshmi P
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2022
Subjects
Analysis
Arbitration
Artificial neural networks
Attorneys
Classification
Computational linguistics
Constitutional law
Data processing
Information retrieval
Language processing
Law
Laws, regulations and rules
Legal services
Legislation
Machine learning
Natural language interfaces
Neural networks
Problem solving
China
India
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Summary:An extensive quantity of online statistics accessible in the legal domain has made legal data processing the main sector of research development. A broad variety of problems, including legal document categorization, information extraction, and prediction have been put into a scope of legitimate system issues. The utilization of digitalized based inventive support has multi-fold advantages for the legal counsel community. These advantages comprise decreasing the laborious human task complicated in observant, extracting the relevant information, reducing the charge and time by-way-of automation, solving problems without the participation of law court otherwise with smaller period and attempt, arbitrating the constitution law for law professionals as well everyday users and building recommendations found on predictive analysis, which possibly examined additional perfect. In this chapter, we are analyzing the adaptation of various deep learning methods in the legal domain focusing on three main tasks namely text classification, information extraction, and prediction.
ISSN:1548-3673
1548-3681
DOI:10.4018/IJeC.301257