## 3 State-of-the-Art
### Human Rights  
Several initiatives have been proposed to study the legal corpus constituted by the ECHR judgments. The focus of the study is on data mining analysis techniques for predicting violations or infringements of the European Convention on Human Rights (ECHR) articles. For instance, in 2016, a group of American and British law and computer science researchers developed a predictive algorithm for the ECHR [33]. This experiment was the first to train the SVM model for predictive analysis of the textual content of court decisions. The algorithm predicted whether there had been a violation of Article 3 (prohibition of torture and inhuman and degrading treatment), Article 6 (right to a fair trial), and Article 8 (respect for family and private life) of the ECHR. The algorithm was developed to predict violations of these articles exclusively based on information extracted from the text of the judgments concerning the description of the facts, applicable laws and the parties' arguments. The procedure, facts, circumstances and the relevant law of the cases were extracted from 584 decisions. Furthermore, they use a law violation of a given article of the Convention. The proposal achieves between 62% and 79% of accuracy in the decisions of the cases studied. An important conclusion was that the most relevant information for achieving the results was the description of the facts included in the circumstances of the case variable. In the same vein, [PERSON] et al. [34] used 11532 ECtHR judgment documents to train an SVM linear classifier to predict future judicial decisions. A prediction accuracy of 75% was obtained for the violation of nine ECHT articles. The approach emphasizes the potential of ML techniques in the legal field. However, they showed that predicting decisions of future cases based on past cases negatively impacts performance (average accuracy range of 58-68%). Nevertheless, they demonstrated that relatively high classification performance could be achieved (average accuracy of 65%) by predicting outcomes solely based on the surnames of the judges hearing the cases. [PERSON], [PERSON], and [PERSON] performed another study on the same dataset. They used the ANGELIC methodology to predict the outcomes of cases [35]. The experimental results were successful because they could predict court decisions with high accuracy (an average of 79%). The study used a dataset comprising 10 cases related to Article 6 of the ECtHR. The study concludes that Prolog can predict decisions with high accuracy and that this model can be adapted as the law evolves. Furthermore, [PERSON] and [PERSON] [36] used 584 documents to compare five ML techniques--k-NN, logistic regression, bagging, random forests and SVM--to predict ECtHR judicial decisions. The authors used spectral clustering and N-grams to extract textual representations of topics, and they concluded that the SVM outperforms the other models. The authors of [37] also predict law violation and non-violation using auto-sklearn to build models for 12 articles in the ECHR. The authors used n-grams, word embeddings (echr2 vec) and doc2 vec for the vectorization task for training Gradient Boosting (GB), Random Forest (RF), Stochastic Gradient Descent (SGD), Decision tree (DT), and Quadratic Discriminant Analysis (QDA). The authors obtained an average of 68.83% accuracy for the different models. In a similar vein, [PERSON] et al. [38] propose a system to automatically predict judicial decisions using nine articles of the ECHR. Specifically, they propose an approach to automatically predict two possible classes, such as 'violation' of an article and 'non-violation'. After solving the unbalanced-classes problem, they vectorize the documents using the Term frequency-inverse term frequency (TF-IDF) normalization. Then words or tokens were grouped into bigrams, 3-grams, and 4-grams to improve the classification task using the SVM technique.