mlsanityData to 14 Oct 2026

Challenges and Considerations in Annotating Legal Data: A Comprehensive Overview

arXiv 2407.17503 ↗First posted 5 Jul 2024cs.IR cs.CL cs.LGcited by 2

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Abstract (arXiv)

The process of annotating data within the legal sector is filled with distinct challenges that differ from other fields, primarily due to the inherent complexities of legal language and documentation. The initial task usually involves selecting an appropriate raw dataset that captures the intricate aspects of legal texts. Following this, extracting text becomes a complicated task, as legal documents often have complex structures, footnotes, references, and unique terminology. The importance of data cleaning is magnified in this context, ensuring that redundant information is eliminated while maintaining crucial legal details and context. Creating comprehensive yet straightforward annotation guidelines is imperative, as these guidelines serve as the road map for maintaining uniformity and addressing the subtle nuances of legal terminology. Another critical aspect is the involvement of legal professionals in the annotation process. Their expertise is valuable in ensuring that the data not only remains contextually accurate but also adheres to prevailing legal standards and interpretations. This paper provides an expanded view of these challenges and aims to offer a foundational understanding and guidance for researchers and professionals engaged in legal data annotation projects. In addition, we provide links to our created and fine-tuned datasets and language models. These resources are outcomes of our discussed projects and solutions to challenges faced while working on them.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 5 Jul 2024.

Builds on

2 of 14 references.

References · 2

  1. 2020Towards an Open Platform for Legal Informationcited by 19
  2. 2023German BERT Model for Legal Named Entity Recognitioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.