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

Abstract

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.