mlsanityData to 7 Oct 2026

CrimeNER Demo: Named-Entity Recognition in the Crime Domain

arXiv 2607.14800 ↗First posted 16 Jul 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 16 Jul 2026.

Builds on

15 of 22 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  5. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  6. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 32
  7. 2022E-NER -- An Annotated Named Entity Recognition Corpus of Legal Textcited by 13
  8. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 10
  9. 2025Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairscited by 6
  10. 2025Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)cited by 6
  11. 2026Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2cited by 6
  12. 2026Auditing Training Data in Domain-adapted LLMs: LoRA-MINTcited by 3
3 more
  1. 2026Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyondcited by 3
  2. 2026Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Datasetcited by 2
  3. 2026Comparative Study of Domain-adapted VLMs for General Document Visual Question Answeringcited by 2

Built on by

Not cited yet.