Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.5753/lasdigov.2025.9117

Authors: Ana Clara B. Borges, Mayara C. Marinho, Rodrigo de Freitas Nogueira, Jacir L. Bordim, Vinicius R. P. Borges

Institution: Universidade de Brasília

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico

Award: G2823776494