Skip to content

Latest commit

 

History

History
87 lines (60 loc) · 10.2 KB

README.md

File metadata and controls

87 lines (60 loc) · 10.2 KB

Legal IR and NLP: the History, Challenges, and State-of-the-Art

This repository contains all detailed information and resources for our tutorial at ECIR 2023, held at Dublin, Ireland (April 2023).

Abstract

Artificial Intelligence (AI), Machine Learning (ML), Information Retrieval (IR) and Natural Language Processing (NLP) are transforming the way legal professionals and law firms approach their work. The significant potential for the application of AI to Law, for instance, by creating computational solutions for legal tasks, has intrigued researchers for decades. This appeal has only been amplified with the advent of Deep Learning (DL). It is worth noting that working with legal text is far more challenging than in many other subdomains of IR/NLP, mainly due to factors like lengthy documents, complex language and lack of large-scale datasets. In this tutorial, we shall introduce the audience to the nature of legal systems and texts, and the challenges associated with processing legal documents. We shall then touch upon the history of AI and Law research, and how it has evolved over the years from rudimentary approaches to DL techniques. There will also be a brief introduction into the recent, state-of-the-art research in general domain IR and NLP. We shall then discuss in more detail about specific IR/NLP tasks in the legal domain and their solutions, available tools and datasets, as well as the industry perspective. This will be followed by a hands-on coding/demo session, which is likely to be of great practical benefit to the attendees.

Tutorial Outline

Part Topic Presenter Link to Slides
1 Background on legal text Saptarshi Ghosh Slides
2 Brief history of AI-Law and important milestones Jack G. Conrad Slides
3 Background on NLP and IR Pawan Goyal Slides
4 State-of-the-art survey Debasis Ganguly, Paheli Bhattacharya and Kripabandhu Ghosh Slides
5 Industry perspective Jack G. Conrad Slides
6 Future directions, advent of LLMs and explainability Jack G. Conrad, Kripabandhu Ghosh and Saptarshi Ghosh Slides
7 Hands-on coding Debasis Ganguly, Paheli Bhattacharya, Shounak Paul and Shubham Kumar Nigam JuPyter Notebook

Task-specific Resources

This section contains resources for different automation tasks in the legal domain

Legal Named Entity Recognition

This task aims to identify different entities in legal documents. Entities may be classified into different groups that have different legal meanings, such as the parties (appellants, respondents), lawyers, judges and so on.

Legal Summarization

The task of summarization in the legal domain aims to generate a gist of the entire case document, either in extractive fashion (selecting the most important sentences) or abstractive fashion (similar to summaries written by humans).

Legal Judgment Prediction

Broadly speaking, this task aims to determine the outcomes of court cases. In many settings, this may be composed of several sub-tasks, which are addressed in the forthcoming sections.

Identifying Legal Articles and Charges

Often considered a sub-task of Legal Judgment Prediction, this task aims to identify the relevant legal articles and charges given the facts of a case.

Semantic Segmentation / Rhetorical Role Labeling

Court case documents are composed of several functional parts such as Facts, Arguments, Ruling, etc. which may not be clearly demarcated. This task aims to automate the process of segmenting a court case document into these parts.

Pre-trained Language Models for the Legal Domain

Recently there have been many efforts to pre-train large, transformer-based language models for the legal domain, which have been adapted to many down-stream end tasks with spectacular efficiency.

General Resources / Benchmarks

This is a miscellaneous list of other resources.

Presenters

  • Debasis Ganguly, Lecturer (Assistant Professor), School of Computing Science, University of Glasgow, Glasgow, Scotland
  • Jack G. Conrad, Director of Applied Research, Thomson Reuters Labs, Minneapolis, MN USA
  • Kripabandhu Ghosh, Assistant Professor, Department of Computational & Data Sciences, IISER Kolkata, West Bengal, India
  • Saptarshi Ghosh, Assistant Professor, Department of Computer Science & Engineering, IIT Kharagpur, West Bengal, India
  • Pawan Goyal, Associate Professor, Deptt. of Computer Science & Engineering, IIT Kharagpur, West Bengal, India
  • Paheli Bhattacharya, NLP Research Architect, Bosch Research, India
  • Shubham Kumar Nigam, Senior Research Fellow, Department of Computer Science & Engineering, IIT Kanpur, Uttar Pradesh, India
  • Shounak Paul, Senior Research Fellow, Department of Computer Science & Engineering, IIT Kharagpur, West Bengal, India