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arXiv · 2603.02150

Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset

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

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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Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Daniel DeAlcala, Gonzalo Mancera, Javier Irigoyen, Ruben Tolosana, Oscar Delgado, Francisco Jurado, Alvaro Ortigosa. 2026-03-02. Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset. https://arxiv.org/abs/2603.02150

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