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English Information

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Authors
# Name
1 Jesus Yepez(jesus.rojas@inf.ufrgs.br)
2 Bruno Tavares(bruno.tsantos@inf.ufrgs.br)
3 Fabíola Peres(fabioladecarvalholeite@gmail.com)
4 Karin Becker(karin.becker@inf.ufrgs.br)

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Reference
# Reference
1 Betti, L., Abrate, C., and Kaltenbrunner, A. (2023). Large scale analysis of gender bias and sexism in song lyrics. EPJ Data Science, 12(1):10.
2 Brilhante, A. V. M., Giaxa, R. R. B., Branco, J. G. d. O., and Vieira, L. J. E. d. S. (2019). Cultura do estupro e violência ostentação: uma análise a partir da artefactualidade do funk. Interface-Comunicação, Saúde, Educacação, 23:e170621.
3 Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022.
4 Calcina, Erik e Novak, E. (2022). Measuring the similarity of song artists using topic mo- delling. In Proc. of the 25th Intl. Multiconference Information Society - Data Mining and Data Warehouses (SiKDD), page 103–106.
5 Devi, M. D. and Saharia, N. (2020). Exploiting topic modelling to classify sentiment from lyrics. In Proc. of the 2nd Intl. Conferemce on Machine Learning, Image Processing, Network Security and Data Sciences (MIND), pages 411–423.
6 Grootendorst, M. (2022). BERTopic: Leveraging bert and topic modeling for efficient document clustering. https://maartengr.github.io/BERTopic/index. html.
7 Junior, J. S., Rossi, R., and Lobato, F. (2019). Uma abordagem baseada em letras para a descoberta de conhecimento da música brasileira: o sertanejo como um estudo de caso. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 949–960.
8 Lopes, A. C. (2011). Funk-se Quem Quiser: No Batidão Negro Da Cidade Carioca. Bom Texto FAPERJ.
9 Oramas, S., Espinosa-Anke, L., G´omez, F., and Serra, X. (2018). Natural language pro- cessing for music knowledge discovery. Journal of New Music Research, 47:365–382.
10 Pengfei Liu, Weizhe Yuan, J. F. Z. J. H. H. and Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey ofprompting methods in natural language processing. ACMCom-put., 55(9):35.
11 Peres, F. C. (2023). Puta ou santa: as relações com mulheres enquanto elemento constituinte das masculinidades do funk brasileiro? In Anais do IV Encontro Anual de Antropologia do Mercosul.
12 Pham, C. M., Hoyle, A., Sun, S., Resnik, P., and Iyyer, M. (2024). Topicgpt: A prompt-based topic modeling framework. https://doi.org/10.48550/ arXiv.2311.01449.
13 Ramon Pires, Hugo Abonizio, T. S. A. and Nogueira, R. (2023). Sab´ıa: Portuguese large language models. Anais da XII Brazilian Conference on Intelligent Systems, 12(1):15.
14 Roder, M., Both, A., and Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining, pages 399–408.
15 Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., and Hashimoto, T. B. (2024). Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics, 12:39–57.