Infraestrutura em Nuvem para Experimentação Científica em Larga Escala: Relato de Experiência de um Projeto sobre Classificação Automática de Texto
Resumo
Este artigo apresenta um relato de experiência sobre o uso de computação em nuvem no projeto Comparando a Efetividade de Abordagens Neurais e Não-Neurais em Tarefas de Classificação Automática de Texto, desenvolvido entre 2020 e 2022 com apoio do CNPq e da AWS. A infraestrutura em nuvem viabilizou uma avaliação em larga escala, com múltiplas representações textuais, algoritmos e coleções, totalizando mais de 10.000 execuções em instâncias otimizadas para GPU, CPU e memória. O relato destaca a importância da elasticidade da nuvem, da escolha adequada de instâncias e da análise conjunta entre efetividade e custo computacional.
Referências
Aggarwal, C. C. and Zhai, C. (2012). A survey of text classification algorithms. In Mining Text Data, pages 163–222.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Canuto, S., Sousa, D. X., Gonçalves, M. A., and Rosa, T. C. (2018). A thorough evaluation of distance-based meta-features for automated text classification. IEEE Transactions on Knowledge and Data Engineering, 30(12):2242–2256.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins,W. S., Almeida, J. M., Rosa, T., Rocha, L., and Gonçalves, M. A. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171–4186.
Gomes, C., Goncalves, M., Rocha, L., and Canuto, S. (2021). On the cost-effectiveness of stacking of neural and non-neural methods for text classification: Scenarios and performance prediction. In ACL-IJCNLP 2021. Association for Computational Linguistics.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735–1780.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of ECML, pages 137–142.
Lin, J. (2019). The neural hype and comparisons against weak baselines. ACM SIGIR Forum, 52(2):40–51.
Mendes, L. F., Gonçalves, M. A., Cunha, W., Rocha, L. C., Rosa, T. C., and Martins, W. (2020). Keep it simple, lazy: Metalazy, a new metastrategy for lazy text classification. In Proceedings of CIKM, pages 1125–1134.
Shen, D., Wang, G., Wang, W., Min, M. R., Su, Q., Zhang, Y., Li, C., Henao, R., and Carin, L. (2018). Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. In Proceedings of ACL, pages 440–450.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in nlp. In Proceedings of ACL, pages 3645–3650.
Tang, J., Qu, M., and Mei, Q. (2015). Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of KDD, pages 1165–1174.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, pages 5754–5764.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of NAACL-HLT, pages 1480–1489.
Yao, L., Mao, C., and Luo, Y. (2019). Graph convolutional networks for text classification. In Proceedings of AAAI, pages 7370–7377.
Zhang, X., Zhao, J., and LeCun, Y. (2016). Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems, pages 649–657.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Canuto, S., Sousa, D. X., Gonçalves, M. A., and Rosa, T. C. (2018). A thorough evaluation of distance-based meta-features for automated text classification. IEEE Transactions on Knowledge and Data Engineering, 30(12):2242–2256.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins,W. S., Almeida, J. M., Rosa, T., Rocha, L., and Gonçalves, M. A. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171–4186.
Gomes, C., Goncalves, M., Rocha, L., and Canuto, S. (2021). On the cost-effectiveness of stacking of neural and non-neural methods for text classification: Scenarios and performance prediction. In ACL-IJCNLP 2021. Association for Computational Linguistics.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735–1780.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of ECML, pages 137–142.
Lin, J. (2019). The neural hype and comparisons against weak baselines. ACM SIGIR Forum, 52(2):40–51.
Mendes, L. F., Gonçalves, M. A., Cunha, W., Rocha, L. C., Rosa, T. C., and Martins, W. (2020). Keep it simple, lazy: Metalazy, a new metastrategy for lazy text classification. In Proceedings of CIKM, pages 1125–1134.
Shen, D., Wang, G., Wang, W., Min, M. R., Su, Q., Zhang, Y., Li, C., Henao, R., and Carin, L. (2018). Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. In Proceedings of ACL, pages 440–450.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in nlp. In Proceedings of ACL, pages 3645–3650.
Tang, J., Qu, M., and Mei, Q. (2015). Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of KDD, pages 1165–1174.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, pages 5754–5764.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of NAACL-HLT, pages 1480–1489.
Yao, L., Mao, C., and Luo, Y. (2019). Graph convolutional networks for text classification. In Proceedings of AAAI, pages 7370–7377.
Zhang, X., Zhao, J., and LeCun, Y. (2016). Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems, pages 649–657.
Publicado
19/07/2026
Como Citar
ROCHA, Leonardo; CUNHA, Washington; MANGARAVITE, Vitor; GONÇALVES, Marcos André.
Infraestrutura em Nuvem para Experimentação Científica em Larga Escala: Relato de Experiência de um Projeto sobre Classificação Automática de Texto. In: SIMPÓSIO DE INFRAESTRUTURA DIGITAL/NUVEM PARA PESQUISA (PESQUISA@NUVEM), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 59-67.
DOI: https://doi.org/10.5753/pesquisanuvem.2026.21905.
