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- W4316036006 abstract "O objetivo deste trabalho é avaliar modelos de Aprendizado de Máquina (AM) na tarefa de classificação de comunicados de irregularidades em Ouvidorias Públicas de Tribunais de Contas. De maneira geral, pretendese contribuir de forma efetiva para melhorar a triagem desses comunicados, possibilitando maior celeridade na resposta ao cidadão. Devido ao desbalanceamento do dataset original, foram aplicadas técnicas de redimensionamento de dados antes da etapa de treinamento dos modelos. Algoritmos clássicos de Machine Learning (Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbor e Support Vector Machine) foram comparados com o modelo de Deep Learning Bidirectional Encoder Representations from Transformers (BERT) e variações de representação dos textos com Word Embeddings. Os melhores resultados foram obtidos pelo modelo BERT com o dataset redimensionado, atingindo 96% na métrica F1-Score." @default.
- W4316036006 created "2023-01-14" @default.
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- W4316036006 date "2022-11-28" @default.
- W4316036006 modified "2023-09-27" @default.
- W4316036006 title "Classification of Irregularity Communications in Public Ombudsmen Using Supervised Learning Algorithms" @default.
- W4316036006 doi "https://doi.org/10.5753/eniac.2022.227178" @default.
- W4316036006 hasPublicationYear "2022" @default.
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