@inproceedings{cabrera-diego-gheewala-2024-psilence,
    title = "{PSILENCE}: A Pseudonymization Tool for International Law",
    author = "Cabrera-Diego, Luis Adri{\'a}n  and
      Gheewala, Akshita",
    editor = {Volodina, Elena  and
      Alfter, David  and
      Dobnik, Simon  and
      Lindstr{\"o}m Tiedemann, Therese  and
      Mu{\~n}oz S{\'a}nchez, Ricardo  and
      Szawerna, Maria Irena  and
      Vu, Xuan-Son},
    booktitle = "Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.caldpseudo-1.4/",
    pages = "25--36",
    abstract = "Since the announcement of the GDPR, the pseudonymization of legal documents has become a high-priority task in many legal organizations. This means that for making public a document, it is necessary to redact the identity of certain entities, such as witnesses. In this work, we present the first results obtained by PSILENCE, a pseudonymization tool created for redacting semi-automatically international arbitration documents in English. PSILENCE has been built using a Named Entity Recognition (NER) system, along with a Coreference Resolution system. These systems allow us to find the people that we need to redact in a clustered way, but also to propose the same pseudonym throughout one document. This last aspect makes it easier to read and comprehend a redacted legal document. Different experiments were done on four different datasets, one of which was legal, and the results are promising, reaching a Macro F-score of up to 0.72 on the legal dataset."
}