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 Emerson Araújo(emerson.diego@academico.ifpb.edu.br)
2 Diego Pessoa(diego.pessoa@ifpb.edu.br)
3 Hildeberg Albuquerque(hidelberg.albuquerque@ufrpe.br)

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

Reference
# Reference
1 Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT models for Brazilian Portuguese. In Intelligent Systems: 9th Brazilian Conference (BRACIS 2020), pages 403-417. Springer.
2 Albuquerque, H. O., Costa, R., Silvestre, G., Souza, E., da Silva, N. F. F., Vitório, D., Moriyama, G., Martins, L., Soezima, L., Nunes, A., Siqueira, F., Tarrega, J. P., Beinotti, J. V., Dias, M., Silva, M., Gardini, M., Silva, V., de Carvalho, A. C. P. L. F., and Oliveira, A. L. I. (2022). UlyssesNER-Br: A corpus of Brazilian legislative documents for named entity recognition. In Computational Processing of the Portuguese Language (PROPOR 2022), pages 3-14, Fortaleza, Brazil. Springer.
3 Tran, H. T. H., Chatterjee, N., Pollak, S., and Doucet, A. (2024). DeBERTa beats behemoths: A comparative analysis of fine-tuning, prompting, and PEFT approaches on LegalLensNER. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 371-380, Miami, FL, USA. Association for Computational Linguistics.
4 Albuquerque, H. O., Souza, E., Lucena, D. C. G., Albuquerque, H. J. O., Silva, N. F. F. d., Dias, M. d. S., Nunes, R. O., Oliveira, A. L. I., and Carvalho, A. C. P. L. F. d. (2026). UlyssesLegalNER-Br: from legislative to legal, a comprehensive corpus of Brazilian legal documents for named entity recognition. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 331-341, Salvador, Brazil. Association for Computational Linguistics.
5 Vithanage, D., Yu, P., Xie, Q., Xu, H., Wang, L., and Deng, C. (2025). A comprehensive evaluation of large language models for information extraction from unstructured electronic health records in residential aged care. Computers in Biology and Medicine, 197:111013.
6 Andrade, C. M. V. d., França, C., Belém, F., Jallais, G., Ganem, M. A. S., Teixeira, G., Laender, A. H. F., and Gonçalves, M. A. (2023). PromptNER: Uma abordagem para reconhecimento de entidades nomeadas em dados sensíveis a partir de instâncias rotuladas automaticamente. In Anais do XXXVIII Simpósio Brasileiro de Bancos de Dados (SBBD 2023), pages 269-281, Belo Horizonte, MG, Brasil. SBC.
7 Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., and Hu, X. (2025). Data-centric artificial intelligence: A survey. ACM Computing Surveys, 57(5).
8 Araujo, P. H. L. d., Campos, T. E. d., Oliveira, R. R. R. d., Stauffer, M., Couto, S., and Bermejo, P. (2018). LeNER-Br: a dataset for named entity recognition in Brazilian legal text. In Computational Processing of the Portuguese Language (PROPOR 2018), pages 313-323. Springer.
9 Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., and Sun, M. (2020). How does NLP benefit legal system: A summary of legal artificial intelligence. arXiv preprint arXiv:2004.12158.
10 Araujo, P. H. L. d., Campos, T. E. d., Oliveira, R. R. R. d., Stauffer, M., Couto, S., and Bermejo, P. (2018). LeNER-Br: a dataset for named entity recognition in Brazilian legal text. In Computational Processing of the Portuguese Language (PROPOR 2018), pages 313-323. Springer.
11 Ariai, F., Mackenzie, J., and Demartini, G. (2025). Natural language processing for the legal domain: A survey of tasks, datasets, models, and challenges. ACM Computing Surveys, 58(6):1-37.
12 Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018 (Lei Geral de Proteção de Dados Pessoais).
13 Brito, A. M., Pinheiro, V., Furtado, V., Monteiro Neto, J. A., Bomfim, F. d. C. J., da Costa, A. C. F., Silveira, R., and Aragão, N. (2023). CDJUR-BR - Uma Coleção Dourada do Judiciário Brasileiro com Entidades Nomeadas Refinadas. In Proceedings of the 14th Brazilian Symposium in Information and Human Language Technology (STIL 2023), pages 176-195, Belo Horizonte, Brazil. Association for Computational Linguistics.
14 Castro, P. V. Q. d. (2019). Aprendizagem profunda para reconhecimento de entidades nomeadas em domínio jurídico. Master's thesis, Universidade Federal de Goiás, Goiânia.
15 Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46.
16 Dias, M., Boné, J., Ferreira, J. C., Ribeiro, R., and Maia, R. (2020). Named entity recognition for sensitive data discovery in Portuguese. Applied Sciences, 10(7):2303.
17 Ding, B., Qin, C., Zhao, R., Luo, T., Li, X., Chen, G., Xia, W., Hu, J., Luu, A. T., and Joty, S. (2024). Data augmentation using LLMs: Data perspectives, learning paradigms and challenges. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1679-1705, Bangkok, Thailand. Association for Computational Linguistics.
18 Jurafsky, D. and Martin, J. H. (2024). Speech and Language Processing. Pearson, 3rd ed. draft edition.
19 Maffeo, G., Silva, C., and Oliveira, H. G. (2026). Prompt engineering for named entity extraction from Portuguese legal documents. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 1092-1097, Salvador, Brazil. Association for Computational Linguistics.
20 Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., and Cotterell, R. (2023). Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12284-12314. Association for Computational Linguistics.
21 Nunes, R. O. (2025). Data contamination in specialized named entity recognition corpora. Master's thesis, Universidade Federal do Rio Grande do Sul, Porto Alegre.
22 Nunes, R. O., Balreira, D. G., Spritzer, A. S., and Freitas, C. M. D. S. (2024). A named entity recognition approach for Portuguese legislative texts using self-learning. In Proceedings of the 16th International Conference on Computational Processing of Portuguese (PROPOR 2024), pages 290-300. Springer.
23 Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT models for Brazilian Portuguese. In Intelligent Systems: 9th Brazilian Conference (BRACIS 2020), pages 403-417. Springer.
24 Tran, H. T. H., Chatterjee, N., Pollak, S., and Doucet, A. (2024). DeBERTa beats behemoths: A comparative analysis of fine-tuning, prompting, and PEFT approaches on LegalLensNER. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 371-380, Miami, FL, USA. Association for Computational Linguistics.
25 Vithanage, D., Yu, P., Xie, Q., Xu, H., Wang, L., and Deng, C. (2025). A comprehensive evaluation of large language models for information extraction from unstructured electronic health records in residential aged care. Computers in Biology and Medicine, 197:111013.
26 Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., and Hu, X. (2025). Data-centric artificial intelligence: A survey. ACM Computing Surveys, 57(5).
27 Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., and Sun, M. (2020). How does NLP benefit legal system: A summary of legal artificial intelligence. arXiv preprint arXiv:2004.12158.