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Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges (2410.21306v1)

Published 25 Oct 2024 in cs.CL and cs.AI
Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges

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 LLMs, we analyze both developed LLMs and approaches for adapting general LLMs 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.

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Natural Language Processing for the Legal Domain: A Comprehensive Survey

The paper "Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges" offers an extensive analysis of the application of NLP in the legal field. It underlines how NLP reshapes legal practices by aiding in computational tasks, presenting detailed insights into various specialized tasks within Legal NLP, including Legal Document Summarization (LDS), Legal Named Entity Recognition (NER), Legal Question Answering (LQA), Legal Text Classification (LTC), Legal Judgment Prediction (LJP), and more.

Tasks and Methodological Approaches in Legal NLP

The paper outlines the unique complexity of legal texts, emphasizing lengthy documents, nuanced language, and limited open-access datasets, all of which pose significant challenges to NLP systems. This complexity demands refined approaches that can handle the distinct properties of legal language. Legal NLP encompasses specific tasks such as:

  • Legal Document Summarization (LDS): The summarization task must account for the structured and formal nature of legal documents, with techniques ranging from extractive to abstractive summarization approaches.
  • Legal Named Entity Recognition (NER): Recognizing entities within legal documents involves identifying various specific entities, including legal acts, case law, statutes, and more. This task requires sophisticated methods adapted to the intricacies of legal language.
  • Legal Question Answering (LQA): LQA tasks necessitate models to understand and interpret complex legal questions and answer with precise legal information. The paper discusses several studies employing models like transformers and BERT for efficient task execution.
  • Legal Text Classification (LTC): Text classification involves categorizing legal documents into predefined categories, leveraging sophisticated classification algorithms to handle the substantial and complex label spaces inherent in legal databases.
  • Legal Judgment Prediction (LJP): Predicting outcomes of legal cases using historical data is a critical area of focus. The survey outlines various models and methods applied in large-scale legal datasets.

Datasets and LLMs

The research highlights the importance of specialized datasets and tailored LLMs for the legal domain. It provides an overview of numerous datasets used for legal NLP tasks, detailing their construction and adaptation for different legal systems and jurisdictions.

Furthermore, the development and adaptation of LLMs (LMs) for legal tasks form a critical part of this research. Models such as legal-bert, Lawformer, SauLLM-7B, and Legal-LM are explored, demonstrating the need for domain-specific LMs trained on specialized legal corpora. The integration of legal specific knowledge via KG is also discussed, enhancing the models' capacity to deliver accurate and contextually relevant legal insights.

Challenges and Future Directions

While NLP offers transformative capabilities to legal processes, the paper identifies key challenges such as the inherent biases of AI applications, the need for sophisticated, robust, and interpretable models, and the challenge of processing complex legal language and reasoning. The challenge of fairness and transparency in AI decisions remains paramount, given the potential impacts on the rights and lives of individuals involved.

The paper concludes with proposed future directions, underscoring the necessity for more comprehensive datasets, enhanced legal text processing methods, and more nuanced approaches to integrate legal reasoning within AI systems. It suggests areas for further research, such as expanding multilingual capabilities and incorporating ethical considerations like bias mitigation and fairness to ensure the responsible deployment of AI in legal contexts.

This survey serves as a critical resource for researchers and practitioners in the legal NLP field, addressing the current capabilities, datasets, and technological challenges while paving the way for advancements in the efficient and fair application of AI in legal practices.

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Authors (2)
  1. Farid Ariai (2 papers)
  2. Gianluca Demartini (34 papers)
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