Jusbrasil e os Desafios Tecnológicos para Facilitar e Aprimorar o Acesso à Justiça
Resumo
Este artigo apresenta uma breve introdução a alguns dos desafios tecnológicos enfrentados na empresa Jusbrasil em sua missão de facilitar o acesso à justiça no Brasil. A Jusbrasil busca unir direito e tecnologia para que a justiça ultrapasse as fronteiras dos tribunais e chegue às casas de qualquer cidadão ou cidadã. Milhões de pessoas acessam a plataforma da empresa atualmente, havendo acesso de mais de 900 mil advogados. Por outro lado, nossa base de informação possui bilhões de documentos contendo artefatos relacionados ao direito no Brasil. Essa base de informação traz oportunidades para o desenvolvimento de produtos inteligentes, tais como sistemas de busca eficazes e eficientes, ferramentas de estruturação e tratamento da informação, mineração de dados e sistemas de recomendação, dentre outros. Neste trabalho, discutimos e apresentamos para a comunidade acadêmica alguns dos desafios tecnológicos enfrentados pela empresa no dia-a-dia.
Palavras-chave:
busca, tecnologias jurídicas, recuperação de informação
Referências
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Guo, J., Fan, Y., Pang, L., Yang, L., Ai, Q., Zamani, H., Wu, C., Croft, W. B., and Cheng, X. (2019). A deep look into neural ranking models for information retrieval. arXiv preprint arXiv:1903.06902.
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Lucchese, C., Nardini, F. M., Perego, R., Orlando, S., and Trani, S. (2018). Selective gradient boosting for effective learning to rank. In Proceedings of 41st SIGIR, pages 155–164. ACM.
Mesquita, F., da Silva, A. S., de Moura, E. S., Calado, P., and Laender, A. H. (2007).Labrador: Efficiently publishing relational databases on the web by using keyword based query interfaces. Information processing & management, 43(4):983–1004.
Nóbrega, C. and Marinho, L. (2019). Towards explaining recommendations through local surrogate models. In Proceedings of the 34th ACM SIGAPP, pages 1671–1678.
Petri, M., Moffat, A., Mackenzie, J., Culpepper, J. S., and Beck, D. (2019). Accelerated query processing via similarity score prediction. In Proceedings of the 42nd SIGIR, pages 485–494.
Ribeiro, M. T., Ziviani, N., Moura, E. S. D., Hata, I., Lacerda, A., and Veloso, A. (2014). Multiobjective pareto-efficient approaches for recommender systems. ACM TIST, 5(4):1–20.
Rossi, C., de Moura, E. S., Carvalho, A. L., and da Silva, A. S. (2013). Fast documentat-a-time query processing using two-tier indexes. In Proceedings of the 36th SIGIR, pages 183–192.
Silva, S. D. N., De Moura, E. S., Calado, P. P., and Da Silva, A. S. (2020). Effective lightweight learning-to-rank method using unified term impacts. IEEE Access, 8:70420–70437.
Silva, T. P. C., de Moura, E. S., Cavalcanti, J. M. B., da Silva, A. S., de Carvalho, M. G., and Gonc¸alves, M. A. (2009). An evolutionary approach for combining different sources of evidence in search engines. Information Systems, 34(2):276–289.
Sousa, D. X., Canuto, S., Gonc¸alves, M. A., Rosa, T. C., and Martins,W. S. (2019). Risksensitive learning to rank with evolutionary multi-objective feature selection. ACM TOIS, 37(2):24:1–24:34.
Vidal, M. L., da Silva, A. S., de Moura, E. S., and Cavalcanti, J. M. (2008). Structurebased crawling in the hidden web. J. UCS, 14(11):1857–1876.
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., and Sun, M. (2018). Legal judgment prediction via topological learning. In Proceedings of EMNLP, pages 3540–3549.
Collins, A., Tkaczyk, D., Aizawa, A., and Beel, J. (2018). Position bias in recommender systems for digital libraries. In International Conference on Information, pages 335–344. Springer.
Cortez, E., Oliveira, D., da Silva, A. S., de Moura, E. S., and Laender, A. H. (2011). Joint unsupervised structure discovery and information extraction. In Proceedings of the 2011 ACM SIGMOD, pages 541–552.
Cristo, M., Calado, P., de Moura, E. S., Ziviani, N., and Ribeiro-Neto, B. (2003). Link information as a similarity measure in web classification. In International Symposium on String Processing and Information Retrieval. SPIRE, pages 43–55. Springer.
Daoud, C. M., de Moura, E. S., Fernandes, D., da Silva, A. S., Rossi, C., and Carvalho, A. (2017). Waves: a fast multi-tier top-k query processing algorithm. Information Retrieval Journal, 20(3):292–316.
De Freitas, J., Pappa, G. L., da Silva, A. S., Gonc, M. A., Moura, E., Veloso, A., Laender, A. H., de Carvalho, M. G., et al. (2010). Active learning genetic programming for record deduplication. In IEEE Congress on Evolutionary Computation, pages 1–8.
Guo, J., Fan, Y., Pang, L., Yang, L., Ai, Q., Zamani, H., Wu, C., Croft, W. B., and Cheng, X. (2019). A deep look into neural ranking models for information retrieval. arXiv preprint arXiv:1903.06902.
Kluttz, D. N. and Mulligan, D. K. (2019). Automated decision support technologies and the legal profession. Berkeley Tech. LJ, 34:853.
Krishnan, S., Patel, J., Franklin, M. J., and Goldberg, K. (2014). A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems, pages 137–144.
Li, S., Zhang, H., Ye, L., Guo, X., and Fang, B. (2019). Mann: A multichannel attentive neural network for legal judgment prediction. IEEE Access, 7:151144 151155.
Lucchese, C., Nardini, F. M., Perego, R., Orlando, S., and Trani, S. (2018). Selective gradient boosting for effective learning to rank. In Proceedings of 41st SIGIR, pages 155–164. ACM.
Mesquita, F., da Silva, A. S., de Moura, E. S., Calado, P., and Laender, A. H. (2007).Labrador: Efficiently publishing relational databases on the web by using keyword based query interfaces. Information processing & management, 43(4):983–1004.
Nóbrega, C. and Marinho, L. (2019). Towards explaining recommendations through local surrogate models. In Proceedings of the 34th ACM SIGAPP, pages 1671–1678.
Petri, M., Moffat, A., Mackenzie, J., Culpepper, J. S., and Beck, D. (2019). Accelerated query processing via similarity score prediction. In Proceedings of the 42nd SIGIR, pages 485–494.
Ribeiro, M. T., Ziviani, N., Moura, E. S. D., Hata, I., Lacerda, A., and Veloso, A. (2014). Multiobjective pareto-efficient approaches for recommender systems. ACM TIST, 5(4):1–20.
Rossi, C., de Moura, E. S., Carvalho, A. L., and da Silva, A. S. (2013). Fast documentat-a-time query processing using two-tier indexes. In Proceedings of the 36th SIGIR, pages 183–192.
Silva, S. D. N., De Moura, E. S., Calado, P. P., and Da Silva, A. S. (2020). Effective lightweight learning-to-rank method using unified term impacts. IEEE Access, 8:70420–70437.
Silva, T. P. C., de Moura, E. S., Cavalcanti, J. M. B., da Silva, A. S., de Carvalho, M. G., and Gonc¸alves, M. A. (2009). An evolutionary approach for combining different sources of evidence in search engines. Information Systems, 34(2):276–289.
Sousa, D. X., Canuto, S., Gonc¸alves, M. A., Rosa, T. C., and Martins,W. S. (2019). Risksensitive learning to rank with evolutionary multi-objective feature selection. ACM TOIS, 37(2):24:1–24:34.
Vidal, M. L., da Silva, A. S., de Moura, E. S., and Cavalcanti, J. M. (2008). Structurebased crawling in the hidden web. J. UCS, 14(11):1857–1876.
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., and Sun, M. (2018). Legal judgment prediction via topological learning. In Proceedings of EMNLP, pages 3540–3549.
Publicado
18/07/2021
Como Citar
MOURA, Edleno Silva de; COSTA, Rafael; JORDÃO, Gabriel; MAIA, Gustavo Barreto.
Jusbrasil e os Desafios Tecnológicos para Facilitar e Aprimorar o Acesso à Justiça. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 207-213.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15824.
