智能提取犯罪文档实体，支持定制化训练
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
- 基于犯罪数据库的预训练实体识别模型
- 可上传自定义标注数据微调模型
- 一键自动抽取与标注犯罪相关实体
我们提出CrimeNER Demo，一个基于AI的演示平台，可从文书自动提取通用犯罪信息并分类为两级粒度的实体类型。平台提供在CrimeNER数据库上预训练的命名实体识别模型，并支持用户上传自行标注的数据以微调适配特定场景。该演示系统包含三项核心功能：一、犯罪领域预训练的NER模型；二、基于用户自注数据的模型微调能力；三、自动化文档实体抽取与标注流程。项目源码、使用教程及视频演示均开源于GitHub，旨在推动犯罪领域命名实体识别研究，并为研究人员和执法机构提供实用工具。
原文摘要 · Abstract (English)
We present CrimeNER Demo, an AI-powered platform that enables us to extract general crime-related information from documents and classify them into entity types with two levels of granularity. We provide pretrained NER models on the CrimeNER database, and we give the possibility to users to provide their own annotated data to train models for their own specific cases. This demonstrator aims to promote crime-related NER research and provides a practical tool to automatically extract crime information for researchers and law enforcement agencies. The demonstrator includes: i) Pretrained NER models on the crime domain; ii) Possibility to finetune the models on specific data annotated by the user; and iii) An automatic pipeline to extract and annotate crime entities from documents. The demo platform, a tutorial to run the demo, and a video demonstration are publicly available on GitHub.
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