Tutorial on Legal Text Analytics

Legal research is a difficult and crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. By extracting targeted information, NLP-powered applications have the potential to significantly speed up this process. In this tutorial, we'll outline the most recent findings in legal text analytics with an emphasis on Indian judicial records.

The use of information technology can significantly reduce the number of court cases that are pending in India. Reducing court backlogs is a necessary first step toward increasing the accessibility of justice for the common person and the transparency of the judicial system. One of the successful projects in the nation built on free and open-source software is the e-Courts mission mode project, which was launched in 2005. Initiatives in this area are anticipated to expand access to the legal profession's expertise and generate significant societal benefits.

Natural language processing (NLP) has made revolutionary strides in recent years, igniting the burgeoning field of Legal Artificial Intelligence (Legal AI). Legal AI aims to use NLP technology to create tools for the automatic extraction of desired information to assist stakeholders in the legal domain. The objectives and future prospects of NLP research in Legal AI are described in Zhong et al 2020.

With an emphasis on Indian legal text, we seek to deliver state-of-the-art legal text analytics in this tutorial. We'll discuss large language models (LLMs), which are frequently employed for summarizing legal documents, question-answering, text classification, and creating knowledge graphs from legal documents, as well as NLP techniques for analyzing legal documents that are supported by neural networks. We'll present the four aforementioned topics in the context of Indian court judgments, which are the most common documents available in Indian legal proceedings. We will highlight the main ongoing initiatives in this field as well as the research efforts done by the Indian NLP community. We anticipate that the seminar will inspire young scientists, particularly those engaged in NLP and text mining research.