Natural Language Processing Approaches for Accrediting Students on Extracurricular Activities

The undergraduate programs at Brazilian universities allow students to include extracurricular activities in their academic transcripts. The large amount of proof documents (certificates and declarations) submitted by students that are subsequently analyzed by the academic staff makes the accrediting of extracurricular activities time-consuming and prone to error. This paper describes a methodology to classify academic proof documents according to the pre-defined groups by the Universidade de Brasília regulations for extracurricular activities accreditation. Experimental results showed that TF-IDF with SVM outperformed BERT, CNN and BiLSTM with 0.94 average Macro F1-Score, though their performances' difference were not statistically significant.