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English Information

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Authors
# Name
1 Altigran da Silva(alti@icomp.ufam.edu.br)
2 Luisa Pereira Novaes(luisa.novaes@icomp.ufam.edu.br)
3 Daniela Vianna(dvianna@icomp.ufam.edu.br)

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Reference
# Reference
1 Rabelo, J. et al. (2022). Semantic-based classification of relevant case law. In New Frontiers in Artificial Intelligence - JSAI-isAI, pages 84–95
2 Chalkidis, I. et al. (2020). Legal-bert: The muppets straight out of law school.
3 Sansone, C. and Sperlí, G. (2022). Legal information retrieval systems: State-of-the-art and open issues. Information Systems, 106:101967.
4 Devlin, J. et al. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno- logies, NAACL-HLT, pages 4171–4186
5 Silveira, R. et al. (2021). Topic modelling of legal documents via legal-bert1. In Proceedings http://ceur-ws org ISSN, 1613:0073
6 Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure.
7 Vianna, D. and Moura, E. (2022). Organizing portuguese legal documents through topic discovery. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 3388–3392
8 Jalilifard, A. et al. (2021). Semantic sensitive tf-idf to determine word relevance in documents. In Advances in Computing and Network Communications: Proceedings of CoCoNet, pages 327–337
9 Vianna, D., Moura, E., and Silva, A. (2023). A topic discovery approach for unsupervised organization of legal document collections. Artificial Intelligence and Law, pages 1–30
10 Le, Q. and , T. M. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on International Conference on Machine Learning - ICML, page II–1188–II–1196.
11 Mandal, A. et al. (2021). Unsupervised approaches for measuring textual similarity between legal court case reports. Artif. Intell. Law, 29(3):417–451
12 McInnes, L. et al. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2:205
13 McInnes, L. and Healy, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction
14 Nanda, R. et al. (2017). Legal information retrieval using topic clustering and neural networks. In 4th Competition on Legal Information Extraction and Entailment (COLIEE), pages 68–78
15 Park, L. A. et al. (2009). The sensitivity of latent dirichlet allocation for information retrieval. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD, pages 176–188
16 Rabelo, J. et al. (2022). Semantic-based classification of relevant case law. In New Frontiers in Artificial Intelligence - JSAI-isAI, pages 84–95
17 Sansone, C. and Sperlí, G. (2022). Legal information retrieval systems: State-of-the-art and open issues. Information Systems, 106:101967.
18 Silveira, R. et al. (2021). Topic modelling of legal documents via legal-bert1. In Proceedings http://ceur-ws org ISSN, 1613:0073
19 Vianna, D. and Moura, E. (2022). Organizing portuguese legal documents through topic discovery. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 3388–3392
20 Vianna, D., Moura, E., and Silva, A. (2023). A topic discovery approach for unsupervised organization of legal document collections. Artificial Intelligence and Law, pages 1–30