CrimeNER Demo: Named-Entity Recognition in the Crime Domain

Miguel Lopez-Duran; Julian Fierrez; Aythami Morales; Daniel DeAlcala; Gonzalo Mancera; Javier Irigoyen; Ruben Tolosana; Oscar Delgado; Francisco Jurado; Alvaro Ortigosa

arXiv · 2607.14800

CrimeNER Demo: Named-Entity Recognition in the Crime Domain

Miguel Lopez-Duran·Julian Fierrez·Aythami Morales·Daniel DeAlcala·Gonzalo Mancera·Javier Irigoyen·Ruben Tolosana·Oscar Delgado·Francisco Jurado·Alvaro Ortigosa

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Abstract

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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Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Daniel DeAlcala, Gonzalo Mancera, Javier Irigoyen, Ruben Tolosana, Oscar Delgado, Francisco Jurado, Alvaro Ortigosa. 2026-07-16. CrimeNER Demo: Named-Entity Recognition in the Crime Domain. https://arxiv.org/abs/2607.14800

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