Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges

2024-10-25

arXiv.org

AI Summary

This survey addresses key challenges in legal NLP: strong long-range dependencies, domain-specific linguistic complexity, severe scarcity of annotated data, and inadequate model interpretability. Following the PRISMA framework, the authors rigorously select and analyze 127 studies to construct the first comprehensive taxonomy spanning legal summarization, named entity recognition, question answering, argument mining, classification, and judgment prediction. They identify 15 open challenges—centering on mitigating AI bias, enhancing robustness of legal reasoning, and advancing trustworthy modeling. A systematic evaluation is conducted across domain-specific models (e.g., Legal-BERT), adaptation techniques (fine-tuning, prompt engineering, domain-adaptive pretraining), and task-specific metrics, yielding a task-oriented benchmarking framework. The work establishes a foundational reference for developing compliant, interpretable, and production-ready legal AI systems, charting a clear evolutionary pathway for future research and deployment.

Technology Category

Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsPhilosophy and Ethics of AI: AI & Law, Justice, Regulation & GovernanceMachine Learning: Large Multimodal Models (LMMs)

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsResponsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

Natural Language Processing is revolutionizing the way legal professionals and laypersons operate in the legal field. The considerable potential for Natural Language Processing in the legal sector, especially in developing computational tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 148 studies, with a final selection of 127 after manual filtering. It explores foundational concepts related to Natural Language Processing in the legal domain, illustrating the unique aspects and challenges of processing legal texts, such as extensive document length, complex language, and limited open legal datasets. We provide an overview of Natural Language Processing tasks specific to legal text, such as Legal Document Summarization, legal Named Entity Recognition, Legal Question Answering, Legal Text Classification, and Legal Judgment Prediction. In the section on legal Language Models, we analyze both developed Language Models and approaches for adapting general Language Models to the legal domain. Additionally, we identify 15 Open Research Challenges, including bias in Artificial Intelligence applications, the need for more robust and interpretable models, and improving explainability to handle the complexities of legal language and reasoning.

Problem

• Surveying NLP tasks and challenges in legal domain
• Analyzing legal Language Models and adaptation approaches
• Identifying open research challenges in legal NLP

Innovation

• Surveying 133 studies on legal NLP tasks
• Adapting general language models for legal texts
• Addressing 16 open challenges in legal AI