Deep Learning has been widely used for tackling challenging natural language processing
tasks over the recent years. Similarly, the application of Deep Neural Networks in legal …

Natural language processing (NLP) based approaches have recently received attention for
legal systems of several countries. It is of interest to study the wide variety of legal systems …

The surge in legal text production has amplified the workload for legal professionals, making
many tasks repetitive and time-consuming. Furthermore, the complexity and specialized …

Automatically understanding the rhetorical roles of sentences in a legal case judgement is
an important problem to solve, since it can help in several downstream tasks like …

Legal documents are unstructured, use legal jargon, and have considerable length, making
them difficult to process automatically via conventional text processing techniques. A legal …

The task of rhetorical role labeling is to assign labels (such as Fact, Argument, Final
Judgement, etc.) to sentences of a court case document. Rhetorical role labeling is an …

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention
and control of crimes associated with COVID-19 are critical for controlling the pandemic …

Word embeddings have become important building blocks that are used profoundly in
natural language processing (NLP). Despite their several advantages, word embeddings …

Unlike the courts in western countries, public records of Indian judiciary are completely
unstructured and noisy. No large scale publicly available annotated datasets of Indian legal …

Summarizing legal decisions requires the expertise of law practitioners, which is both time-
and cost-intensive. This paper presents techniques for extractive summarization of legal …