2026 EACL EACL 2026

Toward Automatic Delegation Extraction in Japanese Law

Abstract

AbstractThe legal systems have a hierarchical structure, and a higher-level law often authorizes a lower-level law to implement detailed provisions, which is called delegation. When interpreting legal texts with delegation, readers must repeatedly consult the lower-level laws that stipulate the detailed provisions, imposing a substantial workload. Therefore, it is necessary to develop a system that enables readers to instantly refer to relevant laws in delegation. However, manually annotating delegation is difficult because it requires extensive legal expertise, careful reading of numerous legal texts, and continuous adaptation to newly enacted laws. In this study, we focus on Japanese law and develop a two-stage pipeline system for automatic delegation annotation. First, we extract keywords that indicate delegation using a named entity recognition approach. Second, we identify the delegated provision corresponding to each keyword as an entity disambiguation task. In our experiments, the proposed system demonstrates sufficient performance to assist manual annotation in practice.

🧭 Keyword Pioneer — delegation extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio