We present CrimeNER Demo, an AI-powered platform designed to extract general crime-related information from documents and classify them into entity types with two levels of granularity. The platform offers pretrained Named Entity Recognition (NER) models on the CrimeNER database and allows users to provide their own annotated data to train models for 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 demo platform includes:
- Pretrained NER models on the crime domain;
- The possibility to finetune the models on specific user-annotated data;
- 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.
Blogger's Review: The launch of CrimeNER Demo marks a significant advancement in the field of crime information extraction. By combining pretrained models with the ability for users to customize data, it greatly enhances flexibility and accuracy in real-world applications. This platform not only provides tools for research but also offers substantial assistance to law enforcement agencies in handling cases.