A YOLOv8x, TrOCR, and GLiNER pipeline converts Italian Supreme Court PDFs into an anonymized topic-modeling dataset, but the reported improvement over OCR-only is not supported by the paper's own Table 13.
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions
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abstract
Recent advances in Natural Language Processing have demonstrated the effectiveness of pretrained language models like BERT for a variety of downstream tasks. We present GiusBERTo, the first BERT-based model specialized for anonymizing personal data in Italian legal documents. GiusBERTo is trained on a large dataset of Court of Auditors decisions to recognize entities to anonymize, including names, dates, locations, while retaining contextual relevance. We evaluate GiusBERTo on a held-out test set and achieve 97% token-level accuracy. GiusBERTo provides the Italian legal community with an accurate and tailored BERT model for de-identification, balancing privacy and data protection.
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cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
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A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court
A YOLOv8x, TrOCR, and GLiNER pipeline converts Italian Supreme Court PDFs into an anonymized topic-modeling dataset, but the reported improvement over OCR-only is not supported by the paper's own Table 13.