Article Details

Title · Authors · Abstract · Keywords · DOI · References

Construction of an Entity-Relation Corpus for Drug-Related Criminal Cases

LI Linying, QU Yunping, FU Zixuan, LIU Meixi

(School of Software, Dalian University of Foreign Languages, Dalian 116044, China)

Section：Artificial Intelligence · Vol. 2026, Issue 12 · Pages：51-54,60 · 2096-4706（2026）12-0051-05 · CLC number：TP391.1 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.12.010

Abstract

Publicly available structured textual data on drug-related criminal cases remain scarce, hindering data-driven research in this domain. To bridge this gap, this study collects drug-related criminal judgments from China Judgments Online and constructs a structured corpus with fine-grained entity and relation annotations. The annotation schema covers entities such as drug types, involved individuals, and behavioral patterns, as well as semantic relations including drug trafficking, possession, and illegal harboring. We establish a detailed annotation protocol and ensure data quality through an iterative multi-round labeling process with cross-validation. Statistical analysis reveals class imbalance in the annotated corpus: entity types such as personal names occur frequently, whereas certain relations (e.g., possession) are relatively sparse. The resulting corpus provides high-quality foundational data for downstream natural language processing tasks in drug crime analysis, including named entity recognition, relation extraction, and intelligent early-warning systems.

Keywords

entity relation; corpus; judicial documents; annotation guidelines

References

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Funding

Basic Research Project for Higher Education Institutions of Liaoning Provincial Department of Education (LJKMZ20221549)

About the authors

Li Linying (1975-), male, Han ethnicity, from Dalian, Liaoning, holds a PhD and is a professor with a research focus on natural language processing.