Abstract
The COVID-19 pandemic created new demands, not only for health services, but also for services in other domains such as the judicial system. New tools that assist in the analysis of the judicial process may help in this problem. In particular, artificial intelligence (AI) techniques may be applied to provide a qualitative analysis of legal documents. Although there exist a number of works that apply AI in the judicial domain, few target the pandemic or publicly provide the information extracted from the texts. Following the suggestions and needs of a legal expert, we have developed the COVID-19 Portal. It extracts documents from the Supreme Federal Court in Brazil, and applies AI technologies to obtain fine-grained quantitative and qualitative information on words used in the texts. This information is made available on a website and can help lawyers identify trends and develop arguments for judicial processes related to the pandemic.
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In clustering evaluation using silhouette metric, the best value is 1, and the worst value is \(-1\). Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.
References
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc., Sebastopol (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Carriere, J., et al.: Case report: utilizing AI and NLP to assist with healthcare and rehabilitation during the COVID-19 pandemic. Front. Artif. Intell. 4 (2021)
Cinelli, M., et al.: The COVID-19 social media infodemic. arXiv preprint arXiv:2003.05004 (2020)
Fersini, E., Messina, E., Archetti, F., Cislaghi, M.: Semantics and machine learning: a new generation of court management systems. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds.) Knowledge Discovery, Knowledge Engineering, and Knowledge Management, vol. 272, pp. 382–398. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-29764-9_26
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Liu, J., Han, J., Aggarwal, C., Reddy, C.: Spectral clustering (2013)
Lu, Q., Conrad, J.G., Al-Kofahi, K., Keenan, W.: Legal document clustering with built-in topic segmentation. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 383–392. ACM (2011)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14, pp. 281–297 (1967)
McCarty, L.T.: Deep semantic interpretations of legal texts. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 217–224. ACM (2007)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2–4 May 2013, Workshop Track Proceedings (2013). arXiv:1301.3781
Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014). https://doi.org/10.1007/s00357-014-9161-z
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(85), 2825–2830 (2011). http://jmlr.org/papers/v12/pedregosa11a.html
Reimers, N., Gurevych, I.: Making monolingual sentence embeddings multilingual using knowledge distillation. arXiv preprint arXiv:2004.09813 (2020)
Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 1–21 (2017)
Singh, L., et al.: A first look at COVID-19 information and misinformation sharing on Twitter. arXiv preprint arXiv:2003.13907 (2020)
Wagh, R.S.: Knowledge discovery from legal documents dataset using text mining techniques. Int. J. Comput. Appl. 66(23), 32–34 (2013)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S., et al.: Constrained k-means clustering with background knowledge. In: ICML, vol. 1, pp. 577–584 (2001)
Walker, V.R., Han, J.H., Ni, X., Yoseda, K.: Semantic types for computational legal reasoning: propositional connectives and sentence roles in the veterans’ claims dataset. In: Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law, ICAIL 2017, London, United Kingdom, 12–16 June 2017, pp. 217–226 (2017). https://doi.org/10.1145/3086512.3086535
Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
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Sodré, A., Magalhães, D., Floriano, L., Pozo, A., Hara, C., Machado, S. (2021). COVID-19 Portal: Machine Learning Techniques Applied to the Analysis of Judicial Processes Related to the Pandemic. In: Bellatreche, L., et al. New Trends in Database and Information Systems. ADBIS 2021. Communications in Computer and Information Science, vol 1450. Springer, Cham. https://doi.org/10.1007/978-3-030-85082-1_10
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