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English Information

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Authors
# Name
1 Washington Cunha(washingtoncunha@dcc.ufmg.br)
2 Leonardo Rocha(lcrocha@ufsj.edu.br)
3 Marcos André Gonçalves(mgoncalv@dcc.ufmg.br)

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Reference
# Reference
1 Bianco, Guilherme Dal, Denio Duarte, and Marcos André Gonçalves. "Reducing the user labeling effort in effective high recall tasks by fine-tuning active learning." Journal of Intelligent Information Systems 61.2 (2023): 453-472.
2 Washington Cunha, Felipe Viegas, Celso França, Thierson Rosa, Leonardo Rocha, and Marcos André Gonçalves. 2023. A Comparative Survey of Instance Selection Methods applied to Non-Neural and Transformer-Based Text Classification. ACM Comput. Surv. 55, 13s, Article 265 (December 2023), 52 pages. https://doi.org/10.1145/3582000
3 Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An Effective, Efficient, and Scalable Confidence-based Instance Selection Framework for Transformer-Based Text Classification. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 665–674. https://doi.org/10.1145/3539618.3591638
4 Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., ... & 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, 58(3), 102481.
5 Washington Cunha, Alejandro Moreo Fernández, Andrea Esuli, Fabrizio Sebastiani, Leonardo Rocha, and Marcos André Gonçalves. 2025. A Noise-Oriented and Redundancy-Aware Instance Selection Framework. ACM Trans. Inf. Syst. 43, 2, Article 45 (March 2025), 33 pages. https://doi.org/10.1145/3705000
6 Washington Cunha, Leonardo Rocha, and Marcos André Gonçalves. "A thorough benchmark of automatic text classification: From traditional approaches to large language models." arXiv preprint arXiv:2504.01930 (2025).
7 Nardini, F. M., Rulli, C., Trani, S., & Venturini, R. (2023). Neural network compression using binarization and few full-precision weights. arXiv preprint arXiv:2306.08960.
8 Andrea Pasin, Washington Cunha, Marcos André Gonçalves, and Nicola Ferro. 2024. A Quantum Annealing Instance Selection Approach for Efficient and Effective Transformer Fine-Tuning. In Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '24). Association for Computing Machinery, New York, NY, USA, 205–214. https://doi.org/10.1145/3664190.3672515
9 Siino, Marco, Ilenia Tinnirello, and Marco La Cascia. "Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers." Information Systems 121 (2024): 102342.