构建首个通用犯罪实体识别数据集CrimeNER-db，助力执法信息提取。
Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset
- 定义4类粗粒度、21类细粒度犯罪实体，覆盖恐怖袭击与司法通报场景。
- 含1500+标注文档，验证了模型在零样本与少样本下的泛化能力。
- 适合法律科技、情报分析与信息抽取方向的研究者使用。
从犯罪相关文档中提取关键信息对执法机构至关重要，可视为命名实体识别（NER）任务。然而，真实世界犯罪场景的高质量标注数据严重不足。为此，我们提出CrimeNER，一项犯罪领域NER的案例研究，并构建了包含超过1500份标注文档的通用犯罪领域命名实体识别数据库（CrimeNER-db），数据源自公开的恐怖袭击报告及美国司法部新闻稿。我们定义了4种粗粒度犯罪实体类型和21种细粒度实体类型。通过全监督微调模型以及零样本和少样本实验，评估了数据库的质量与模型泛化能力。该数据集已开源于GitHub。
原文摘要 · Abstract (English)
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 experiments using fully supervised finetuned general NER models and zero- and few-shot experiments to address the generalization capabilities. The database is available on GitHub.
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