{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:29:15Z","timestamp":1769772555021,"version":"3.49.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159892","type":"print"},{"value":"9783032159908","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15990-8_25","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:19Z","timestamp":1769716759000},"page":"363-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pseudo-labeling for\u00a0Multi-label Legal Text Classification"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Freitas","sequence":"first","affiliation":[]},{"given":"Thais","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Guilherme","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Pamella","family":"Edokawa","sequence":"additional","affiliation":[]},{"given":"Ariane","family":"Farias","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"25_CR1","unstructured":"Documenta\u00e7\u00e3o - RAFA 2030. https:\/\/agenda2030rafa.github.io\/rafa_documentacao\/. Accessed 24 June 2025"},{"key":"25_CR2","unstructured":"Ahmed, M.S., Khan, L., Oza, N.C.: Pseudo-label generation for multi-label text classification (2011)"},{"key":"25_CR3","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 1877\u20131901. Curran Associates, Inc. (2020)"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the muppets straight out of law school. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2898\u20132904. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.261","DOI":"10.18653\/v1\/2020.findings-emnlp.261"},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Croce, D., Castellucci, G., Basili, R.: GAN-BERT: generative adversarial learning for robust text classification with a bunch of labeled examples. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2114\u20132119. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.191, https:\/\/aclanthology.org\/2020.acl-main.191\/","DOI":"10.18653\/v1\/2020.acl-main.191"},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"Du, G., Zhang, J., Zhang, N., Wu, H., Wu, P., Li, S.: Semi-supervised imbalanced multi-label classification with label propagation. Pattern Recogn. 150(C) (2024). https:\/\/doi.org\/10.1016\/j.patcog.2024.110358","DOI":"10.1016\/j.patcog.2024.110358"},{"key":"25_CR7","volume-title":"Introduction to Natural Language Processing","author":"J Eisenstein","year":"2019","unstructured":"Eisenstein, J.: Introduction to Natural Language Processing. MIT Press, Cambridge (2019)"},{"key":"25_CR8","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Feng, S.Y., et al.: A survey of data augmentation approaches for NLP. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 968\u2013988. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.84, https:\/\/aclanthology.org\/2021.findings-acl.84\/","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Francia, O., Nunez-del Prado, M., Alatrista-Salas, H.: Survey of text mining techniques applied to judicial decisions prediction. Appl. Sci. 12, 10200 (2022). https:\/\/doi.org\/10.3390\/app122010200","DOI":"10.3390\/app122010200"},{"key":"25_CR11","volume-title":"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow","author":"A G\u00e9ron","year":"2022","unstructured":"G\u00e9ron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O\u2019Reilly Media Inc., Sebastopol (2022)"},{"key":"25_CR12","unstructured":"Grootendorst, M.: Bertopic: neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022)"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Springer, New York, New York (2014)","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"25_CR14","unstructured":"Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edn (2025). https:\/\/web.stanford.edu\/~jurafsky\/slp3\/, online manuscript released January 12, 2025"},{"key":"25_CR15","unstructured":"Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning. MIT Press, Cambridge, Massachusetts, USA (2009)"},{"key":"25_CR16","unstructured":"Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188\u20131196. PMLR (2014)"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Liu, R., Lu, Y., Shi, L., Tan, S.: Research on multi-label semi-supervised learning algorithm based on dual selection criteria. IEEE Access 12, 31357\u201331365 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3369919","DOI":"10.1109\/ACCESS.2024.3369919"},{"issue":"6","key":"25_CR18","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1007\/s40747-021-00512-9","volume":"7","author":"V Mehta","year":"2021","unstructured":"Mehta, V., Bawa, S., Singh, J.: Weclustering: word embeddings based text clustering technique for large datasets. Complex Intell. Syst. 7(6), 3211\u20133224 (2021). https:\/\/doi.org\/10.1007\/s40747-021-00512-9","journal-title":"Complex Intell. Syst."},{"key":"25_CR19","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"Noguti, M., Vellasques, E., Soares\u00a0de Oliveira, L.: A small claims court for the NLP: judging legal text classification strategies with small datasets, pp. 1840\u20131845 (2023). https:\/\/doi.org\/10.1109\/SMC53992.2023.10394189","DOI":"10.1109\/SMC53992.2023.10394189"},{"key":"25_CR21","unstructured":"OpenAI: GPT-4 technical report (2023)"},{"key":"25_CR22","unstructured":"Paszke, A., et al.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, pp. 8024\u20138035. Curran Associates, Inc, Canada (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"25_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-030-61377-8_28","volume-title":"Intelligent Systems","author":"F Souza","year":"2020","unstructured":"Souza, F., Nogueira, R., Lotufo, R.: BERTimbau: pretrained BERT models for Brazilian Portuguese. In: Cerri, R., Prati, R.C. (eds.) BRACIS 2020. LNCS (LNAI), vol. 12319, pp. 403\u2013417. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61377-8_28"},{"key":"25_CR24","unstructured":"Surden, H.: Machine learning and law. Washington Law Rev. 89, 87 (2014). https:\/\/scholar.law.colorado.edu\/faculty-articles\/81"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Taha, K., Yoo, P.D., Yeun, C., Homouz, D., Taha, A.: A comprehensive survey of text classification techniques and their research applications: observational and experimental insights. Comput. Sci. Rev. 54, 100664 (2024)","DOI":"10.1016\/j.cosrev.2024.100664"},{"key":"25_CR26","doi-asserted-by":"publisher","unstructured":"Wang, R., Kwong, S., Wang, X., Jia, Y.: Active k-labelsets ensemble for multi-label classification. Pattern Recogn. 109(C), 107583 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2020.107583","DOI":"10.1016\/j.patcog.2020.107583"},{"key":"25_CR27","unstructured":"Warner, B., et al.: Smarter, better, faster, longer: a modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference (2024). https:\/\/arxiv.org\/abs\/2412.13663"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6382\u20136388. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1670, https:\/\/aclanthology.org\/D19-1670\/","DOI":"10.18653\/v1\/D19-1670"},{"key":"25_CR29","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.T., Le, Q.V.: Unsupervised data augmentation for consistency training. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS \u201920. Curran Associates Inc., Red Hook, NY, USA (2020)"},{"key":"25_CR30","doi-asserted-by":"publisher","unstructured":"Yang, W., Zhang, R., Chen, J., Wang, L., Kim, J.: Prototype-guided pseudo labeling for semi-supervised text classification. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 16369\u201316382. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.904","DOI":"10.18653\/v1\/2023.acl-long.904"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wang, Y.S., Yang, Y.: Generation-driven contrastive self-training for zero-shot text classification with instruction-following LLM. In: Graham, Y., Purver, M. (eds.) Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 659\u2013673. Association for Computational Linguistics, St. Julian\u2019s, Malta (2024). https:\/\/aclanthology.org\/2024.eacl-long.39\/","DOI":"10.18653\/v1\/2024.eacl-long.39"},{"key":"25_CR32","doi-asserted-by":"publisher","unstructured":"Zou, H., Caragea, C.: JointMatch: a unified approach for diverse and collaborative pseudo-labeling to semi-supervised text classification. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7290\u20137301. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.451","DOI":"10.18653\/v1\/2023.emnlp-main.451"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15990-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:23Z","timestamp":1769716763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15990-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159892","9783032159908"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15990-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"There are no conflicts of interest to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}