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CrimeNER Demo is an AI-driven platform designed for extracting and classifying crime-related information from documents using named-entity recognition (NER) techniques. It features pretrained NER models tailored to the crime domain and allows users to fine-tune these models with their own annotated data for specific applications. This tool not only advances crime-related NER research but also provides a practical solution for law enforcement and researchers to automate the extraction of critical crime information.
Fine-tuning NER models on user-specific crime data could revolutionize how law enforcement agencies process and analyze crime-related documents.
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.
