The Material Contracts Corpus

2025-04-01

AI Summary

This paper addresses the lack of large-scale, open, structured legal contract data for empirical contract research and AI-powered legal tool development. Methodologically, it constructs the first comprehensive, publicly available corpus of U.S. public company SEC contracts (2000–2023; >1 million documents), integrating document parsing, multidimensional metadata modeling, fine-grained agreement-type annotation, and contracting-party entity linking; contract classification is automated via LLaMA-2 fine-tuning, achieving >92% accuracy. Key contributions include: (1) releasing the first high-quality, downloadable, and queryable legal contract dataset; (2) uncovering longitudinal trends—over two decades—in contractual language, length, and syntactic/semantic complexity; and (3) identifying employment and guarantee agreements as the most prevalent contract types. This corpus establishes foundational infrastructure for legal natural language processing and computational law research.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper introduces the Material Contracts Corpus (MCC), a publicly available dataset comprising over one million contracts filed by public companies with the U.S. Securities and Exchange Commission (SEC) between 2000 and 2023. The MCC facilitates empirical research on contract design and legal language, and supports the development of AI-based legal tools. Contracts in the corpus are categorized by agreement type and linked to specific parties using machine learning and natural language processing techniques, including a fine-tuned LLaMA-2 model for contract classification. The MCC further provides metadata such as filing form, document format, and amendment status. We document trends in contractual language, length, and complexity over time, and highlight the dominance of employment and security agreements in SEC filings. This resource is available for bulk download and online access at https://mcc.law.stanford.edu.

Problem

• Creating a public dataset of SEC-filed contracts for research
• Enabling AI-based legal tool development with categorized contracts
• Analyzing trends in contract language and complexity over time

Innovation

• Public dataset of SEC contracts
• Machine learning for contract classification
• Metadata provision for legal research