## 3 State-of-the-Art
### Criminal Law
For the neural network, 140 sample documents were used. A word segment process was used in the seven specific criminal categories, which was then increased to 251 keywords. Based on the results, the precision obtained reached 94% in the training samples that used all the segments of the written vectors and were not weighted as Back-Propagation Network (BPN) inputs. Similarly, an accuracy of 67% was achieved in the model that uses all the segments of the written vectors, but weighted vectors were used equally as BPN inputs. Text mining has also been conducted in India. [PERSON] et al. [57] created a corpus from the police department data downloaded from official pages. Classifiers such as KNN and K-Means clustering algorithm were used. These algorithms were used to evaluate the incidence of crime in the following cities: New Delhi, Andhra Pradesh, Jammu, Kashmir, Daman, Diu, Jharkhand, Arunachal Pradesh, and Nhaveli, of which Delhi and Jharkhand were ranked as the cities with the highest incidence of crime. In the case of Delhi, the result is explained by the city's cosmopolitan nature, the lower availability of preventive measures, and the inability of law enforcement. In the case of Jharkhand, the study concludes that the crime incidence is because the area is too remote for police presence. The authors used Root relative squared error (RRSE) to measure the quality of the KNN algorithm, which obtained 67.92%. [PERSON] et al. [58] predicted the charges of crimes, such as fraud, theft, or homicide. They used both the legal articles of statutory norms and judicial decisions. To accomplish this, they compiled judicial documents available on the website of the Chinese government, where they extracted descriptions of facts, articles of law, and the charges of imputation. The corpus comprised 50,000 randomly selected documents, 5000 documents for validation, and 5000 documents for testing. The researchers propose a neural network to predict the indictment charges and the relevant articles that serve the legal basis to justify such charges. The results obtained show the effectiveness of their model for both tasks. A BI-GRU sequence encoder was used in the experiment, an SVM classifier was used for legal article extraction, and SGD was used for training. [PERSON] et al. [59] present CAIL2018, a tool that condenses more than 2.6 million criminal cases obtained from the website of the Chinese Supreme People's Court. This tool uses DL techniques to perform NLP tasks combined with neural networks to predict the results of a trial (PJL--Legal Trial Prediction). They used the following three methods in their experiments: TF-IDF + SVM, to extract word features and SVM with a linear kernel to train the classifier. Fast Text was also used to classify texts based on n-grams and Hierarchical softmax. Finally, CNN was used for text classification and fact description coding. Many results were obtained in terms of predicting imputation charges and law articles with high accuracy. In the same context, [PERSON] et al. [60] compare text representations, such as TF-IDF and Embeddings with different classification algorithms, such as SVM, CNN, and HLSTM, in over 1.2 million documents of criminal cases in China. The authors used the accuracy to measure the performance of the algorithms, obtaining an accuracy between 38.3% and 94.4%. Furthermore, [PERSON] et al. [61] evaluate the judgment rationality of the target case via the judgment results of its similar precedent cases. The authors used Doc2 Vec to represent the 41 418 documents in Chinese, which are the input to a GRU. To evaluate the proposal's performance, the authors used the F-measure, which ranges between 73.6% and 78.7%. [PERSON] and [PERSON] propose a work to predict legal decisions or judgments from the UK [62]. The authors used 188 294 English documents to train supervised learning algorithms, such as SVM, LR, RF, KNN, SLP, and MLP.