Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
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ID: 283283
2024
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Abstract
Natural Language Processing (NLP) is revolutionising the way legal
professionals and laypersons operate in the legal field. The considerable
potential for NLP 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 154 studies, with a final selection of
133 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 length, complex language, and limited
open legal datasets. We provide an overview of NLP tasks specific to legal
text, such as Legal Document Summarisation, legal Named Entity Recognition,
Legal Question Answering, Legal Argument Mining, Legal Text Classification, and
Legal Judgement Prediction. In the section on legal Language Models (LMs), we
analyse both developed LMs and approaches for adapting general LMs to the legal
domain. Additionally, we identify 16 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.
| Reference Key |
demartini2024natural
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| Authors | Farid Ariai; Gianluca Demartini |
| Journal | arXiv |
| Year | 2024 |
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