## 3 State-of-the-Art
### Constitutional Law and Ruling Prediction of Supreme Courts
The authors used 1-gram and 2-gram representations for the linear SVM in the last task of temporal prediction, achieving 73.2% and 73.9% when predicting 7 and 14 classes, respectively. [PERSON] et al. [46] propose a forecasting ruling decision using a hybrid neural network model combining a long-short term memory (LSTM) network with a convolutional neural network (CNN). The authors use 120,506 cases with 27 features from the US Supreme Court ruling. They divide the corpus into 80%, 10%, and 10% for training, validation and testing, respectively. Furthermore, they used 10-fold cross-validation to select the best model. They use oversampling techniques and RFE algorithms to select the best features for handling imbalanced classes and feature selection. After the experiments with different architectures, the best score was an F-measure of 93%. Based on the Spanish language, [PERSON] and [PERSON] [47] propose a model to predict court decisions on child custody. First, they study the factors explaining a court's decision to accord child custody by identifying and labeling factual elements and legal principles from the court rulings. Then, they use ANN to predict whether the custody is joint or sole, based on the relationship between the parents and their economic resources, the child's opinion and the psychological report. They used 1884 court sentences from June 2016 to June 2020 from the Spanish Judicial Authority Documentation Center (CENDO). The best model output predictions with 85% accuracy. A limitation of performing court prediction is that access is limited to the annotated corpus. [PERSON] et al. [48] gathered a dataset of 50,000 legal opinions and their manually labeled legal procedural postures (POSTURE50K). The dataset were gathered from all states and Districts of Columbia in the United States between 2013 and 2020. The authors propose a DL architecture that adopts domain-specific pretraining and a label-attention mechanism for multi-label document classification based on RoBERTa using this dataset. The authors used POSTURE50K and EUROLEX57K to compare their proposal with other algorithms, such as Random Forest, Multi-Layer Perceptron, BI-GRU-LWAN, AttentionXML, APLC_XLNet and X-Transformer. Depending on the dataset, the F-measure result ranges from 74.5% to 80.2%, depending on the dataset. Thus, in some cases, their proposal outperforms other algorithms. Additionally, [PERSON] et al. [49] propose a study to predict decisions in different courts, such as constitutional, civil, criminal, administrative and taxation. They used a corpus consisting of 39,157 legal documents, represented by a reduced Term Frequency vector using the PCA algorithm. The results were used as an input to DT, Random Forest, SVM, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTMs) and Bidirectional LSTM (BILSTM). Furthermore, they add an attention layer to the GRU, LSTM and BILSTM algorithms. The F-measure obtained results range from 56% to 87%. [PERSON], [PERSON] and [PERSON] proposed the eLegPredict system to predict Indian supreme court decisions [50]. The eLegPredict is trained and tested using more than 3072 supreme court cases and the eXtreme Gradient Boosting (XGBoost), ANN, SVM and RF supervised learning algorithms. The system achieved an F-measure between 60% and 76% accuracy (F1-score). [PERSON] et al. [51] describe an experiment using deep neural networks and learning techniques to predict decisions of the Supreme Court of the United States with an accuracy of 70.4% on a corpus made up of 7770 cases and 70,000 judicial votes. This experiment is based on research published by [PERSON], [PERSON] and [PERSON] in 2014, except that they used modern ML techniques instead of using random trees for prediction. RMSprop and Momentum are two techniques to compare results. The proposed approaches include SVM for surface learning and neural networks for DL. The neural network model used is implemented in the Pythons Theano library.