@inproceedings{viksna-skadina-2025-anonymise,
    title = "Anonymise: A Tool for Multilingual Document Pseudonymisation",
    author = "V{\={i}}ksna, Rinalds  and
      Skadina, Inguna",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.154/",
    pages = "1327--1332",
    abstract = "According to the EU legislation, documents containing personal information need to be anonymized before public sharing. However, manual anonymisation is a time-consuming and costly process. Thus, there is a need for a robust text de-identification technique that accurately identifies and replaces personally identifiable information. This paper introduces the Anonymise tool, a system for document de-identification. The tool accepts text documents of various types (e.g., MS Word, plain-text), de-identifies personal information, and saves the de-identified document in its original format. The tool employs a modular architecture, integrating list-based matching, regular expressions and deep-learning-based named entity recognition to detect spans for redaction. Our evaluation results demonstrate high recall rates, making Anonymise a reliable solution for ensuring no sensitive information is left exposed. The tool can be accessed through a userfriendly web-based interface or API, offering flexibility for both individual and large-scale document processing needs. By automating document de-identification with high accuracy and efficiency, Anonymise presents a reliable solution for ensuring compliance with EU privacy regulations while reducing the time and cost associated with manual anonymisation."
}