Evaluation of Named Entity Recognition for Personal Data Discovery in Audio Transcriptions - Munhos, Carlos André Misiuk; Ignaczak, Luciano (Companion Proceedings of the Brazilian Symposium on Cybersecurity (SBSeg); 2025: Companion Proceedings of the 25th Brazilian Symposium on CybersecurityDO - 10.5753/sbseg_estendido.2025.10728, 2025)
The growth of personal data have driven legislation such as Brazil’s LGPD, but the scarcity of annotated data and the inherently unstructured nature of sources like audio transcripts make both training named entity recognition (NER) models and reliably identifying personal data challenging; to address these issues, this work presents the results of applying a fine-tuned version of the Portuguese pre-trained BERTimbau model with synthetic data to identify four entities (Name, CPF, RG, and Address) and extract the relations “Resides in” and “Holds document” achieving an F1-score of 0.98 in the NER task and 0.29 in the relation extraction task.