Predicting Judicial Outcomes in the Brazilian Legal System Using Textual Features

The combination of Natural Language Processing and Artificial Intelligence for the field of Law is a growing area, with the potential of radically changing the daily routine of legal professionals. The amount of text generated by those professionals is outstanding, and to this point, still unexplored by Computer Science. One of the most acclaimed research field covering both knowledge areas is Legal Prediction, in which intelligent systems try to predict specific judicial characteristics, such as the judicial outcome or the judicial class or a given case. This research intends to create a classifier to predict judicial outcomes in the Brazilian legal system. At first, we developed a text crawler to retrieve judicial outcomes from the official Brazilian electronic legal systems. Afterward, a few judicial subjects were selected, and some of their features were extracted. Later, a set of different classifiers was applied to predict the legal considering these textual features.

[1]  D. Katz,et al.  A general approach for predicting the behavior of the Supreme Court of the United States , 2016, PloS one.

[2]  Zhenyu Liu,et al.  A predictive performance comparison of machine learning models for judicial cases , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[3]  Nikolaos Aletras,et al.  Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective , 2016, PeerJ Comput. Sci..

[4]  Balaji Padmanabhan,et al.  A process model for information retrieval context learning and knowledge discovery , 2015, Artificial Intelligence and Law.

[5]  Massimo Poesio,et al.  Automatic deception detection in Italian court cases , 2013, Artificial Intelligence and Law.

[6]  Jonathan P. Kastellec.  The Statistical Analysis of Judicial Decisions and Legal Rules with Classification Trees , 2010 .

[7]  Owen Kufandirimbwa,et al.  Towards Judicial Data Mining: Arguing for Adoption in the Judicial System , 2012 .

[8]  Jorge L. V. Barbosa,et al.  Ontology-based information extraction for juridical events with case studies in Brazilian legal realm , 2017, Artificial Intelligence and Law.

[9]  Y.L.J.A. Remmits,et al.  Finding the Topics of Case Law: Latent Dirichlet Allocation on Supreme Court Decisions , 2017 .

[10]  Maria Fasli,et al.  Deploying a co-training algorithm to classify human-rights abuses , 2017, 2017 International Conference on the Frontiers and Advances in Data Science (FADS).

[11]  Laura Antonucci,et al.  Evaluation of Italian Judicial System , 2014 .

[12]  Marie-Francine Moens,et al.  Argumentation mining , 2011, Artificial Intelligence and Law.

[13]  Benjamin Anthony Albert H Alarie,et al.  How artificial intelligence will affect the practice of law , 2018 .

[14]  Felipe Meneguzzi,et al.  Norm conflict identification in contracts , 2017, Artificial Intelligence and Law.

[15]  Karl Branting,et al.  Inducing Predictive Models for Decision Support in Administrative Adjudication , 2017, AICOL.

[16]  Josef van Genabith,et al.  Predicting the Law Area and Decisions of French Supreme Court Cases , 2017, RANLP.

[17]  Julio Ríos-Figueroa,et al.  Judicial Independence and Corruption: An Analysis of Latin America , 2006 .