This repository serves as a curated collection of essential research papers in the field of Legal Intelligence and Legal AI. It targets researchers, engineers, and practitioners interested in applying Natural Language Processing (NLP) and machine learning techniques to legal documents and processes, offering a comprehensive overview of the state-of-the-art and foundational work.

How It Works

The repository organizes seminal papers across various legal NLP tasks, including judgment prediction, information retrieval, question answering, named entity recognition, and text summarization. It highlights key datasets, methodologies, and findings from each paper, providing a structured entry point into the legal AI research landscape. The collection emphasizes the evolution of NLP applications within the legal system.

Quick Start & Requirements

This repository is a curated list of papers and does not involve direct code execution or installation. Users can access the papers via the provided PDF links.

Highlighted Details

• Comprehensive coverage of Legal AI tasks: Judgment Prediction, Information Retrieval, Question Answering, Named Entity Recognition, Text Summarization, and more.
• Extensive list of datasets for legal NLP research, detailing language, task, and size.
• Includes foundational and recent papers from major NLP conferences (ACL, EMNLP, COLING, LREC, AAAI, etc.).
• Features work from prominent researchers and institutions in the field.

Maintenance & Community

The repository is contributed to by researchers from Tsinghua University (thunlp). Further community engagement details are not specified in the README.

Licensing & Compatibility

The repository itself is a list of links to research papers. The licensing of the individual papers is determined by their respective publishers and venues. Users must adhere to the copyright and usage terms of each linked paper.

Limitations & Caveats

This is a curated list of papers and does not provide executable code, models, or direct access to datasets beyond the paper references. Users will need to locate and potentially access the datasets and codebases mentioned in the papers independently.