Transformer-Based Extraction of Statutory Definitions from the U.S. Code

• URL: http://arxiv.org/abs/2504.16353v1
• Date: Wed, 23 Apr 2025 02:09:53 GMT
• Title: Transformer-Based Extraction of Statutory Definitions from the U.S. Code
• Authors: Arpana Hosabettu, Harsh Shah,
• Abstract summary: We present an advanced NLP system to automatically extract defined terms, their definitions, and their scope from the United States Code (U.S.C.)<n>Our best model achieves 96.8% precision and 98.9% recall (98.2% F1-score)<n>This work contributes to improving accessibility and understanding of legal information while establishing a foundation for downstream legal reasoning tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic extraction of definitions from legal texts is critical for enhancing the comprehension and clarity of complex legal corpora such as the United States Code (U.S.C.). We present an advanced NLP system leveraging transformer-based architectures to automatically extract defined terms, their definitions, and their scope from the U.S.C. We address the challenges of automatically identifying legal definitions, extracting defined terms, and determining their scope within this complex corpus of over 200,000 pages of federal statutory law. Building upon previous feature-based machine learning methods, our updated model employs domain-specific transformers (Legal-BERT) fine-tuned specifically for statutory texts, significantly improving extraction accuracy. Our work implements a multi-stage pipeline that combines document structure analysis with state-of-the-art language models to process legal text from the XML version of the U.S. Code. Each paragraph is first classified using a fine-tuned legal domain BERT model to determine if it contains a definition. Our system then aggregates related paragraphs into coherent definitional units and applies a combination of attention mechanisms and rule-based patterns to extract defined terms and their jurisdictional scope. The definition extraction system is evaluated on multiple titles of the U.S. Code containing thousands of definitions, demonstrating significant improvements over previous approaches. Our best model achieves 96.8% precision and 98.9% recall (98.2% F1-score), substantially outperforming traditional machine learning classifiers. This work contributes to improving accessibility and understanding of legal information while establishing a foundation for downstream legal reasoning tasks.

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