A methodology for extracting key information from crime news

Official crime statistics are often released with significant delays and in an aggregated form that limits their usefulness for real-time monitoring and detailed analysis. In different countries, as happens in Italy, accessing to detailed crime records is further restricted by the confidential nature of police reports. Conversely, online news articles offer an alternative source of information that is rich and timely. However, their unstructured textual content poses challenges for automated data extraction processing. In this paper, we present a Natural Language Processing (NLP) pipeline designed to extract structured information from Italian crime news articles, which has been evaluated on a semi-automatically annotated dataset. Despite the limited amount of annotated data, the proposed approach showed promising results: the topic modeling component, included into the pipeline, facilitated targeted crime analysis, and the Named Entity Recognition (NER) module achieved an overall F1-score of 74%. These results demonstrate the effectiveness of the methodology in low-resource settings and highlight its potential for supporting automated crime analysis based on unstructured news data