An empirical analysis of Brazilian courts law documents using learning techniques
Resumo
This paper describes a survey on investigating judicial data to find patterns and relations between crime attributes and corresponding decisions made by courts, aiming to find import directions that interpretation of the law might be taking. We have developed an initial methodology and experimentation to look for behaviour patterns to build judicial sentences in the scope of Brazilian criminal courts and achieved results related to important trends in decision making. Neural networks-based techniques were applied for classification and pattern recognition, based on Multi-Layer Perceptron and Radial-basis Functions, associated with data organisation techniques and behavioral modalities extraction.Referências
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Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D., and Lampos, V. (2016). Predicting judicial decisions of the european court of human rights: a natural language processing perspective. PeerJ Computer Science, 2:e93.
Borges, F., Borges, R., and Bourcier, D. (2003). Articial neural networks and legal categorization. In The 16th Annual Conference on Legal Knowledge and Information Systems (JURIX'03), page 187.
Branting, K. L. (2017). Automating judicial document analysis. In Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2017), London, UK. CEUR.
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Dash, M. and Liu, H. (1997). Feature selection for classication. Intelligent Data Analysis, 1(1):131 – 156.
Fersini, E., Messina, E., Arosio, G., and Archetti, F. (2009). Audio-based emotion recognition in judicial domain: A multilayer support vector machines approach. In International workshop on machine learning and data mining in pattern recognition, pages 594–602. Springer.
Fonseca, F. F., Cunha, D. M., Vieira, E. O., and Modena, C. M. (2018). Implicações de novas tecnologias na atividade e qualicação dos servidores: Processo judicial eletrônico e a justiça do trabalho. Revista Brasileira de Saúde Ocupacional, 43:1–12.
Giuseppe Contissa, Francesca Lgioia, M. L. H.-W. M. (2018). Towards consumerempowering articial intelligence. In Twenty-Seventh International Joint Conference on Articial Intelligence (IJCAI-18), pages 5150–5157.
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Grimm, C. (2006). Dosimetria da pena utilizando redes neurais.
Haykin, S. (2001). Redes neurais: princípios e prática. Bookman Editora, 2nd edition.
Maranhão, J. (2018). O impacto na justiça. In Yoshida, E., editor, EXAME CEO Inteligência Articial, pages 80–83. Editora Abril.
Orr, M. J. L. (1996). Introduction to radial basis function networks.
Saboya, K. (2014). Ne bis in idem em tempos de multiplicidades de sanções e de agências
de controle punitivo. Jornal de Ciências Criminais, 1:71–92.
Srivastava, N., Mansimov, E., and Salakhutdinov, R. (2015). Unsupervised learning of video representations using lstms. CoRR, abs/1502.04681.
Thammaboosadee, S., Watanapa, B., and Charoenkitkarn, N. (2012). A framework of multi-stage classier for identifying criminal law sentences. volume 13, pages 53 – 59. Proceedings of the International Neural Network Society Winter Conference (INNS-WC2012).
Wang, A. H. (2010). Detecting spam bots in online social networking sites: A machine learning approach. In Foresti, S. and Jajodia, S., editors, Data and Applications Security and Privacy XXIV, pages 335–342, Berlin, Heidelberg. Springer Berlin Heidelberg.
AINow, A. N. I. (2018). Ai now report 2018. Technical report.
Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D., and Lampos, V. (2016). Predicting judicial decisions of the european court of human rights: a natural language processing perspective. PeerJ Computer Science, 2:e93.
Borges, F., Borges, R., and Bourcier, D. (2003). Articial neural networks and legal categorization. In The 16th Annual Conference on Legal Knowledge and Information Systems (JURIX'03), page 187.
Branting, K. L. (2017). Automating judicial document analysis. In Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2017), London, UK. CEUR.
CNJ, C. (2018). Justiça em números analytical report 2018. Technical report. baseline 2017. direito.
Coelho, J. V. d. A. B. R. (2017). Aplicações e implicações da inteligência articial no CongressoNacionalBrasileiro (1940). Decreto-lei no 2.848, de 07 de dezembro de 1940.
Dash, M. and Liu, H. (1997). Feature selection for classication. Intelligent Data Analysis, 1(1):131 – 156.
Fersini, E., Messina, E., Arosio, G., and Archetti, F. (2009). Audio-based emotion recognition in judicial domain: A multilayer support vector machines approach. In International workshop on machine learning and data mining in pattern recognition, pages 594–602. Springer.
Fonseca, F. F., Cunha, D. M., Vieira, E. O., and Modena, C. M. (2018). Implicações de novas tecnologias na atividade e qualicação dos servidores: Processo judicial eletrônico e a justiça do trabalho. Revista Brasileira de Saúde Ocupacional, 43:1–12.
Giuseppe Contissa, Francesca Lgioia, M. L. H.-W. M. (2018). Towards consumerempowering articial intelligence. In Twenty-Seventh International Joint Conference on Articial Intelligence (IJCAI-18), pages 5150–5157.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT press.
Grimm, C. (2006). Dosimetria da pena utilizando redes neurais.
Haykin, S. (2001). Redes neurais: princípios e prática. Bookman Editora, 2nd edition.
Maranhão, J. (2018). O impacto na justiça. In Yoshida, E., editor, EXAME CEO Inteligência Articial, pages 80–83. Editora Abril.
Orr, M. J. L. (1996). Introduction to radial basis function networks.
Saboya, K. (2014). Ne bis in idem em tempos de multiplicidades de sanções e de agências
de controle punitivo. Jornal de Ciências Criminais, 1:71–92.
Srivastava, N., Mansimov, E., and Salakhutdinov, R. (2015). Unsupervised learning of video representations using lstms. CoRR, abs/1502.04681.
Thammaboosadee, S., Watanapa, B., and Charoenkitkarn, N. (2012). A framework of multi-stage classier for identifying criminal law sentences. volume 13, pages 53 – 59. Proceedings of the International Neural Network Society Winter Conference (INNS-WC2012).
Wang, A. H. (2010). Detecting spam bots in online social networking sites: A machine learning approach. In Foresti, S. and Jajodia, S., editors, Data and Applications Security and Privacy XXIV, pages 335–342, Berlin, Heidelberg. Springer Berlin Heidelberg.
Publicado
02/09/2019
Como Citar
SILVA, Bruno; DA COSTA-ABREU, Marjory.
An empirical analysis of Brazilian courts law documents using learning techniques. In: WORKSHOP DE FORENSE COMPUTACIONAL, 8. , 2019, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 11-17.
DOI: https://doi.org/10.5753/wfc.2019.14019.
