Evaluating Joint Named Entity Recognition and Relation Extraction on a Novel Portuguese News Dataset
Resumo
Técnicas de Extração de Informação (EI) transformam dados não estruturados em conhecimento estruturado, útil para construção de grafos de conhecimento e tarefas subsequentes. Reconhecimento de Entidades Nomeadas (NER) e Extração de Relações (ER) são tarefas fundamentais de EI para identificar entidades e relações semânticas em textos. Este trabalho apresenta um novo conjunto de dados de notícias em português anotado para NER e ER em múltiplos domínios. O objetivo é disponibilizar a base e avaliar uma técnica do estado da arte (Partition Filter Network) para essas tarefas. Resultados mostram desempenho consistente na extração conjunta e evidenciam o impacto dos domínios, principalmente em RE, que requer entendimento contextualizado.Referências
Al-Moslmi, T., Gallofre Ocana, M., L. Opdahl, A., and Veres, C. (2020). Named Entity Extraction for Knowledge Graphs: A Literature Overview. IEEE Access, 8:32862–32881.
Bassignana, E., Ginter, F., Pyysalo, S., van der Goot, R., and Plank, B. (2023). Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction. In Alumäe, T. and Fishel, M., editors, Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 80–85, Tórshavn, Faroe Islands. University of Tartu Library.
Blouin, B., Armand, C., and Henriot, C. (2024). A dataset for named entity recognition and entity linking in Chinese historical newspapers. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., and Xue, N., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 385–394, Torino, Italia. ELRA and ICCL.
Celian, R., Gandon, Fabien, Catherine, F., Franck, M., and Hanna, A. A. (2025). A systematic review of relation extraction task since the emergence of Transformers. arXiv:2511.03610 [cs.CL].
De, S., Sanyal, D. K., and Mukherjee, I. (2025). Fine-tuned encoder models with data augmentation beat ChatGPT in agricultural named entity recognition and relation extraction. Expert Systems with Applications, 277:127126.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Version Number: 2.
Giorgi, J., Wang, X., Sahar, N., Shin, W. Y., Bader, G. D., and Wang, B. (2019). End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models. arXiv:1912.13415 [cs].
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
Jurafsky, D. and Martin, J. H. (2026). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. 3rd edition.
King, G. and Zeng, L. (2001). Logistic Regression in Rare Events Data. Political Analysis, 9(2):137–163.
Li, Z., Ma, H., Lv, Y., and Shen, H. (2022). Joint Extraction of Entities and Relations in the News Domain. In Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., and Li, X., editors, Advanced Data Mining and Applications, volume 13725, pages 81–92. Springer Nature Switzerland, Cham. Series Title: Lecture Notes in Computer Science.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2020). Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2):318–327.
Lucas, P. et al. (2023). Bete: A brazilian portuguese dataset for named entity recognition and relation extraction in the diabetes healthcare domain. In Proceedings of the 12th Brazilian Conference on Intelligent Systems (BRACIS 2023), pages 256–267. Springer / Sociedade Brasileira de Computação (SBC).
Luz de Araujo, P. H., de Campos, T. E., de Oliveira, R. R. R., 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), pages 313–321. Springer.
Nakayama, H., Kubo, T., Kamura, J., Taniguchi, Y., and Liang, X. (2018). doccano: Text Annotation Tool for Human.
Nasar, Z., Jaffry, S. W., and Malik, M. K. (2021). Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Comput. Surv., 54(1):20:1–20:39.
Nunes, S., Mario Jorge, A., Amorim, E., Sousa, H., Leal, A., Silvano, P. M., Cantante, I., and Campos, R. (2024). Text2Story lusa: A dataset for narrative analysis in European Portuguese news articles. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., and Xue, N., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15773–15782, Torino, Italia. ELRA and ICCL.
Opdahl, A. L., Al-Moslmi, T., Dang-Nguyen, D.-T., Gallofré Ocaña, M., Tessem, B., and Veres, C. (2023). Semantic Knowledge Graphs for the News: A Review. ACM Computing Surveys, 55(7):1–38.
Popovski, G., Seljak, B. K., and Eftimov, T. (2020). A Survey of Named-Entity Recognition Methods for Food Information Extraction. IEEE Access, 8:31586–31594.
Quinta de Castro, P. V. and Da Silva, N. F. F. (2025). Labor Lex: A New Portuguese Corpus and Pipeline for Information Extraction in Brazilian Legal Texts. In Aletras, N., Chalkidis, I., Barrett, L., Goant, ă, C., Preot,iuc-Pietro, D., and Spanakis, G., editors, Proceedings of the Natural Legal Language Processing Workshop 2025, pages 184–206, Suzhou, China. Association for Computational Linguistics.
Reyes, D., Trajano, D., Manssour, I., Vieira, R., and Bordini, R. (2021). Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence Based on BERT. In Anais da X Brazilian Conference on Intelligent Systems, Porto Alegre, RS, Brasil. SBC.
Santos, D., Cardoso, N., Seco, N., Vilela, R., Rocha, P., Afonso, S., and Barreiro, A. (2006). HAREM: An advanced NER evaluation contest for Portuguese. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), pages 1986–1989, Genoa, Italy. European Language Resources Association (ELRA).
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Ticianeli, G. H. G., Candido Junior, A., Guilherme, I. R., Penteado, B. E., Perrout, S. R., Morelli, L. H., Paiola, P. H., and Garcia, G. L. (2024). Comparison of Relation Extraction Models for Generating Knowledge Graphs in the Oil Drilling Domain. In Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2024), pages 707–718, Brasil. Sociedade Brasileira de Computação - SBC.
Utino, M. Y. R. and Marcacini, R. M. (2025). BERT vs. LLM2Vec: A Comparative Study of Embedding Models for Semantic Information Retrieval. In Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2025), pages 427–438, Brasil. Sociedade Brasileira de Computação - SBC.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is All you Need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Wang, X., Shen, H., Qi, H., and Dai, Y. (2026). Enhancing Low-Resource Joint Entity and Relation Extraction Using Large Language Models within Semi-Supervised Learning. ACM Transactions on Knowledge Discovery from Data, 20(3):1–25.
Wei, J. and Zou, K. (2019). EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6381–6387, Hong Kong, China. Association for Computational Linguistics.
Yan, Z., Zhang, C., Fu, J., Zhang, Q., and Wei, Z. (2021). A Partition Filter Network for Joint Entity and Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 185–197, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C. D. (2017). Position-aware Attention and Supervised Data Improve Slot Filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35–45, Copenhagen, Denmark. Association for Computational Linguistics.
Bassignana, E., Ginter, F., Pyysalo, S., van der Goot, R., and Plank, B. (2023). Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction. In Alumäe, T. and Fishel, M., editors, Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 80–85, Tórshavn, Faroe Islands. University of Tartu Library.
Blouin, B., Armand, C., and Henriot, C. (2024). A dataset for named entity recognition and entity linking in Chinese historical newspapers. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., and Xue, N., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 385–394, Torino, Italia. ELRA and ICCL.
Celian, R., Gandon, Fabien, Catherine, F., Franck, M., and Hanna, A. A. (2025). A systematic review of relation extraction task since the emergence of Transformers. arXiv:2511.03610 [cs.CL].
De, S., Sanyal, D. K., and Mukherjee, I. (2025). Fine-tuned encoder models with data augmentation beat ChatGPT in agricultural named entity recognition and relation extraction. Expert Systems with Applications, 277:127126.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Version Number: 2.
Giorgi, J., Wang, X., Sahar, N., Shin, W. Y., Bader, G. D., and Wang, B. (2019). End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models. arXiv:1912.13415 [cs].
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
Jurafsky, D. and Martin, J. H. (2026). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. 3rd edition.
King, G. and Zeng, L. (2001). Logistic Regression in Rare Events Data. Political Analysis, 9(2):137–163.
Li, Z., Ma, H., Lv, Y., and Shen, H. (2022). Joint Extraction of Entities and Relations in the News Domain. In Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., and Li, X., editors, Advanced Data Mining and Applications, volume 13725, pages 81–92. Springer Nature Switzerland, Cham. Series Title: Lecture Notes in Computer Science.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2020). Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2):318–327.
Lucas, P. et al. (2023). Bete: A brazilian portuguese dataset for named entity recognition and relation extraction in the diabetes healthcare domain. In Proceedings of the 12th Brazilian Conference on Intelligent Systems (BRACIS 2023), pages 256–267. Springer / Sociedade Brasileira de Computação (SBC).
Luz de Araujo, P. H., de Campos, T. E., de Oliveira, R. R. R., 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), pages 313–321. Springer.
Nakayama, H., Kubo, T., Kamura, J., Taniguchi, Y., and Liang, X. (2018). doccano: Text Annotation Tool for Human.
Nasar, Z., Jaffry, S. W., and Malik, M. K. (2021). Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Comput. Surv., 54(1):20:1–20:39.
Nunes, S., Mario Jorge, A., Amorim, E., Sousa, H., Leal, A., Silvano, P. M., Cantante, I., and Campos, R. (2024). Text2Story lusa: A dataset for narrative analysis in European Portuguese news articles. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., and Xue, N., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15773–15782, Torino, Italia. ELRA and ICCL.
Opdahl, A. L., Al-Moslmi, T., Dang-Nguyen, D.-T., Gallofré Ocaña, M., Tessem, B., and Veres, C. (2023). Semantic Knowledge Graphs for the News: A Review. ACM Computing Surveys, 55(7):1–38.
Popovski, G., Seljak, B. K., and Eftimov, T. (2020). A Survey of Named-Entity Recognition Methods for Food Information Extraction. IEEE Access, 8:31586–31594.
Quinta de Castro, P. V. and Da Silva, N. F. F. (2025). Labor Lex: A New Portuguese Corpus and Pipeline for Information Extraction in Brazilian Legal Texts. In Aletras, N., Chalkidis, I., Barrett, L., Goant, ă, C., Preot,iuc-Pietro, D., and Spanakis, G., editors, Proceedings of the Natural Legal Language Processing Workshop 2025, pages 184–206, Suzhou, China. Association for Computational Linguistics.
Reyes, D., Trajano, D., Manssour, I., Vieira, R., and Bordini, R. (2021). Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence Based on BERT. In Anais da X Brazilian Conference on Intelligent Systems, Porto Alegre, RS, Brasil. SBC.
Santos, D., Cardoso, N., Seco, N., Vilela, R., Rocha, P., Afonso, S., and Barreiro, A. (2006). HAREM: An advanced NER evaluation contest for Portuguese. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), pages 1986–1989, Genoa, Italy. European Language Resources Association (ELRA).
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Ticianeli, G. H. G., Candido Junior, A., Guilherme, I. R., Penteado, B. E., Perrout, S. R., Morelli, L. H., Paiola, P. H., and Garcia, G. L. (2024). Comparison of Relation Extraction Models for Generating Knowledge Graphs in the Oil Drilling Domain. In Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2024), pages 707–718, Brasil. Sociedade Brasileira de Computação - SBC.
Utino, M. Y. R. and Marcacini, R. M. (2025). BERT vs. LLM2Vec: A Comparative Study of Embedding Models for Semantic Information Retrieval. In Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2025), pages 427–438, Brasil. Sociedade Brasileira de Computação - SBC.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is All you Need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Wang, X., Shen, H., Qi, H., and Dai, Y. (2026). Enhancing Low-Resource Joint Entity and Relation Extraction Using Large Language Models within Semi-Supervised Learning. ACM Transactions on Knowledge Discovery from Data, 20(3):1–25.
Wei, J. and Zou, K. (2019). EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6381–6387, Hong Kong, China. Association for Computational Linguistics.
Yan, Z., Zhang, C., Fu, J., Zhang, Q., and Wei, Z. (2021). A Partition Filter Network for Joint Entity and Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 185–197, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C. D. (2017). Position-aware Attention and Supervised Data Improve Slot Filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35–45, Copenhagen, Denmark. Association for Computational Linguistics.
Publicado
19/10/2026
Como Citar
MACHADO, Derli Aparecida; PAVELSKI, Lucas Marcondes; BERARDI, Rita Cristina Galarraga.
Evaluating Joint Named Entity Recognition and Relation Extraction on a Novel Portuguese News Dataset. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 373-384.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.29970.
