Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
Farid Ariai, Joel Mackenzie, Gianluca Demartini
TL;DR
This survey comprehensively maps NLP in the legal domain, detailing tasks (LQA, LJP, LTC, LDS, NER, LAM), datasets, corpora, and domain-adapted language models. It follows PRISMA to synthesize 131 studies from 154 identified works, highlighting state-of-the-art approaches and practical challenges such as bias, privacy, and explainability. The work also catalogs large-scale benchmarks and corpora (e.g., LexGLUE, CaseHOLD, CICL; Cambridge Law Corpus; Pile of Law) that drive evaluation and pre-training of legal NLP systems. By outlining 16 open research challenges and providing concrete directions for data, models, and evaluation, the paper offers a roadmap for robust, fair, and interpretable AI-assisted legal practice.
Abstract
Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance 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 154 studies, with a final selection of 131 after manual filtering. It explores foundational concepts related to NLP in the legal domain, illustrating the unique aspects and challenges of processing legal texts, such as extensive document lengths, complex language, and limited open legal datasets. We provide an overview of NLP tasks specific to legal text, such as Document Summarisation, Named Entity Recognition, Question Answering, Argument Mining, Text Classification, and Judgement Prediction. Furthermore, we analyse both developed legal-oriented language models, and approaches for adapting general-purpose language models to the legal domain. Additionally, we identify sixteen open research challenges, including the detection and mitigation of 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.