arXiv cs.AI· Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Ruben Tolosana, Daniel DeAlcala, Gonzalo Mancera, Javier Irigoyen, Oscar Delgado, Francisco Jurado, Alvaro Ortigosa·· 3 小时前AI 评分31
CrimeNER：面向语言智能研究的犯罪领域 NER 数据集与方法
CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence
AI 导读
研究者发布 CrimeNER 犯罪相关命名实体识别数据集（CrimeNER-db），包含超 1.5K 份标注文档，来源为公开恐袭报告与美国司法部新闻稿，定义了 4 类粗粒度和 21 类细粒度犯罪实体。团队通过扁平与嵌套 NER 架构的有监督实验以及 LLM 的零样本和少样本实验验证数据质量。数据集已在 GitHub 开放获取。
正文 · 原文
Abstract:The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named-Entity Recognition (NER) task. However, there is a considerable lack of adequately annotated data on general real-world crime scenarios. To address this issue, we present CrimeNER, a case study of crime-related NER, and a general crime-related Named-Entity Recognition database (CrimeNER-db), consisting of more than 1.5K annotated documents extracted from public reports of terrorist attacks and the US Department of Justice's press notes. We define 4 coarse types of crime entity and 21 fine-grained entity types. We address the quality of the presented database with fully supervised experiments using flat and nested NER architectures and zero- and few-shot experiments using Large Language Models (LLMs). The database is available on GitHub.
| Comments: | 29 pages, 5 figures, preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB) |
| Cite as: | arXiv:2603.02150 [cs.CL] |
| (or arXiv:2603.02150v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.02150 arXiv-issued DOI via DataCite |
Submission history
From: Miguel Lopez-Duran [view email]
[v1]
Mon, 2 Mar 2026 18:12:02 UTC (249 KB)
[v2]
Wed, 8 Jul 2026 09:02:49 UTC (96 KB)
[v3]
Fri, 23 Oct 2026 08:08:45 UTC (182 KB)
来源：arXiv cs.AI · arxiv.org