CrimeNER：面向语言智能研究与调查的犯罪领域命名实体识别数据集与方法

CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence

AI 导读

CrimeNER 发布犯罪相关命名实体识别数据集 CrimeNER-db，包含从恐怖袭击公开报告和美国司法部新闻稿中提取的 1.5K 余份标注文档，定义 4 种粗粒度与 21 种细粒度犯罪实体类型。研究用扁平与嵌套 NER 架构做全监督实验，并用 LLM 做零样本与少样本实验以验证数据质量。数据集已在 GitHub 开放。

正文 · 原文

View PDF HTML (experimental)

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.CL · arxiv.org