## 3 State-of-the-Art
### Criminal Law  
For Criminal Law, reviewed studies used text mining and supervised ML models for managing Judicial files, specifically for treating written criminal sentences in a way that allows the identification of cases. Similar to their classification. In [54], the authors propose a 7-step methodology to classify the accused person's Acquittal and Conviction (classes) from data obtained from criminal cases related to the murder of the Delhi District Court. Thus, 86 cases were vectorized using the Bag of Word strategy and normalized using the min-max method. Then, algorithms such as SVM, kNN, CART, and NB were used. Finally, leave-one-out cross-validation is used to measure the model's effectiveness, obtaining an accuracy of 85-92% and an F1 score of 86-92%. [PERSON] performed another study in Portuguese, and [PERSON] [55]. They compared Logistic Regression (LR), Latent Dirichlet allocation (LDA), KNN, Regression Tree (RT), Gaussian NB (GNB), and SVM algorithm using a corpus consisting of 1562 homicides and corruption in Brazilian court rulings. They obtained an F-measure result ranging from 78% to 98%, depending on the algorithm and court document. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nro.** & **Reference** & **Corpus Size** & **Lang.** & **Model** & **Repr.** & **Metric** & **Min(\%)** & **Max(\%)** & **Year** \\ \hline
1 & [41] & 628 & E & DT & Binary & Accuracy & 68 & 75 & 2004 \\ \hline
2 & [51] & 120,506 & E & LSTM + CNN & Embeddings & Accuracy & 88 & 92.05 & 2015 \\ \hline
3 & [42] & 28,000 & E & RF & Term frequency & Accuracy & 70.2 & 71.9 & 2017 \\ \hline
4 & [45] & 430 & T & SVM &
\begin{tabular}{c} Bag-of- \\ Words, 2-gram, \\ 3-gram \\ \end{tabular} & F-measure & 63 & 90.3 & 2017 \\ \hline
5 & [44] & 27,492 & Ph & SVM, RF &
\begin{tabular}{c} BoW, \\ n-grams \\ \end{tabular} & Accuracy & 55 & 59 & 2018 \\ \hline
6 & [49] & 39,157 & T &
\begin{tabular}{c} GRU, LSTM, \\ and BILSTM \\ \end{tabular} & TF-IDF & F-measure & 56 & 87 & 2021 \\ \hline
7 & [47] & 1884 & S & ANN & Binary & Accuracy & 61 & 85 & 2021 \\ \hline
8 & [43] & 430 & MLP &
\begin{tabular}{c} Embeddings, \\ TF-IDF, \\ BoW \\ \end{tabular} & F-measure & 60 & 98.7 & 2021 \\ \hline
9 & [48] & 50,000 & E & \begin{tabular}{c} RoBERTa, RF, \\ MLP, BIGRU- \\ LWAN, AttentionXML, \\ APLC\_XLNet, \\ and X- \\ Transformer \\ \end{tabular} & 50,000 & E & \begin{tabular}{c} RoBERTa, RF, \\ LMAN, A- \\tentionXML, \\ APLC\_XLNet, \\ and X- \\ Transformer \\ \end{tabular} & 50,000 & E & \
\begin{tabular}{c} RoBERTa, RF, \\ LMAN, A- \\tentionXML, \\ APLC\_XLNet, \\ and X- \\ Transformer \\ \end{tabular} & 50,000 & E &
\begin{tabular}{c} Sequence \\ of words \\ and X- \\ Transformer \\ \end{tabular} & 74.5 & 80.2 & 2021 \\ \hline
10 & [53] & - & E & HCNN & Accuracy & 77.65 & - & 81.13 & 2021 \\ \hline
11 & [50] & 3072 & E &
\begin{tabular}{c} XGBoost, \\ ANN, SVM, and RF \\ \end{tabular} & TF-IDF & F-measure & 60 & 76 & 2021 \\ \hline
12 & [52] & 123,361 & F & JuriBERT & Tokenized & Accuracy & 70.38 & 83.28 & 2021 \\ \hline
13 & [46] & 120,506 & E & CNN, LSTM & Embedding & F-measure & 79 & 93 & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of ruling decision prediction in Constitutional Law and Supreme Courts cases, where E = English, Fa = Farsi, F = French, Ph = Philippine, S = Spanish, and T = Turkish. [PERSON] and [PERSON] [56] developed a document classification, clustering, and search methodology based on neural networks to help law enforcement departments handle written criminal judgments more efficiently. Thus, judges can review similar cases and have a broader view of issuing a sentence. The corpus used to train the models is composed of 210 written criminal sentences from the judiciary of Taiwan for training and testing. Seven crime categories were selected, including homicide, sex crimes, drug-related corruption, computer crime, theft, and fraud. The critical terms used in the trials and files were summarized using 2604 keywords in which 100 main words with the highest frequency of use were selected.