Welcome to the community of
RegNLP
Regulatory Natural Language Processing
A research and practice community for regulatory AI

About

Regulatory texts are complex, lengthy, and constantly evolving. They underpin governance, compliance, and legal frameworks across sectors such as finance, healthcare, construction, and technology. Working with this material requires methods that go beyond generic NLP.

Regulatory Natural Language Processing (RegNLP) focuses on developing and applying NLP techniques that are tailored to regulatory and compliance documents. This includes document parsing, entity and obligation extraction, information retrieval, question answering, summarization, and automated compliance support.

While recent advances in NLP and large language models have created new opportunities, many open challenges remain. How can models robustly handle changing regulations and jurisdiction-specific nuances? How do we extract, link, and reason over obligations spread across large, cross-referenced document collections? How do we evaluate accuracy, faithfulness, and legal reliability in this domain?

The RegNLP community brings together researchers and practitioners from NLP, legal informatics, compliance, governance, and industry to explore these questions, share resources, and build practical solutions for real-world regulatory problems.

Topics

Core areas of research in Regulatory NLP

RegNLP covers original work on regulatory data and on data closely related to compliance and regulation, including but not limited to:

Applications of NLP to regulatory tasks

  • Compliance monitoring and management
  • Risk assessment, reporting, and assurance
  • Detection, interpretation, and classification of regulatory changes
  • Summarization of regulations and guidance for decision support
  • Creation of lexical, annotated, and benchmark resources for the regulatory domain
  • Addressing bias, robustness, and privacy in regulatory data processing

Adapting NLP methods for regulatory data

  • Information retrieval, anomaly detection, and clustering for regulatory corpora
  • Multimodal regulation analysis (e.g., text, tables, forms) and entity linking, recognition, and disambiguation
  • Syntactic processing: tagging, chunking, and parsing of legal and regulatory text
  • Dialogue and discourse analysis for regulatory advice and support systems
  • Text summarization, relation and event extraction in regulatory contexts
  • Question answering over regulations, guidance, and case material
  • Using and adapting large language models for regulatory and compliance tasks

Tasks, resources, and demos: New regulatory tasks, datasets, benchmarks, evaluation metrics, and system descriptions that use NLP to process regulations and related materials.

Industrial research: Case studies, methods, and lessons learned from deploying NLP and LLM-based systems on proprietary regulatory or compliance data.

Interdisciplinary position papers: Critical and forward-looking perspectives on LLMs, legality, ethics, governance, and future directions for the RegNLP field.