Automatic Classification of Access Levels in Documents from the Brazilian Electronic Information System

The Brazilian Electronic Information System (SEI) is used to manage documents and administrative processes in public institutions. Users often need to upload external documents and manually assign an access level: public, restricted, or confidential. To reduce human errors in this process, we propose an automatic classification approach. A labeled corpus was constructed using public SEI’s documents and artificial samples generated from SEI’s form templates filled with fictional content using ChatGPT. We trained and evaluated four classification models: SVM, LSTM, BiLSTM, and BERT. Experiments were performed by means of Stratified K-Fold cross-validation and the results showed that SVM and BiLSTM performed best, each achieving a macro F1-score of 0.96.