SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

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

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Cunha, W., Canuto, S., Viegas, F., Salles, T., Gomes, C., Mangaravite, V., Resende, E., Rosa, T., Gonçalves, M. A., and Rocha, L. (2020). Extended pre-processing pipeline for text classification: On the role of meta-feature representations, sparsification and selective sampling. Information Processing & Management, 57(4):102263.
2 Cunha, W., França, C., Fonseca, G., Rocha, L., and Gonçalves, M. A. (2023a). 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, pages 665–674.
3 Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins, W. S., Almeida, J. M., et al. (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.
4 Cunha, W., Moreo Fernández, A., Esuli, A., Sebastiani, F., Rocha, L., and Gonçalves, M. A. (2025). A noise-oriented and redundancy-aware instance selection framework. ACM Transactions on Information Systems, 43(2):1–33.
5 Cunha, W., Viegas, F., França, C., Rosa, T., Rocha, L., and Gonçalves, M. A. (2023b). A comparative survey of instance selection methods applied to non-neural and transformer-based text classification. ACM Computing Surveys, 55(13s):1–52.
6 DeepSeek et al. (2025). Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.
7 Garcia, S., Derrac, J., Cano, J., and Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence
8 Martins, K., Vaz de Melo, P., and Santos, R. (2021). Why do document-level polarity classifiers fail? In Proceedings of the 2021 Conference of the NAACL: Human Language Technologies.
9 Ng, A. (2016). Nuts and bolts of building ai applications using deep learning. NIPS Keynote Talk, 64.
10 Rajaraman, S., Ganesan, P., and Antani, S. (2022). Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks. PloS one.
11 Roy, A. and Cambria, E. (2022). Soft labeling constraint for generalizing from sentiments in single domain. Knowledge-Based Systems, 245:108346.
12 Uppaal, R., Hu, J., and Li, Y. (2023). Is fine-tuning needed? pre-trained language models are near perfect for out-of-domain detection. arXiv preprint arXiv:2305.13282.