Survey of text mining techniques applied to Judicial decisions prediction
Resumen
This paper reviews the most recent literature on experiments with different Machine
Learning, Deep Learning and Natural Language Processing techniques applied to predict judicial
and administrative decisions. Among the most outstanding findings, we have that the most used
data mining techniques are Support Vector Machine (SVM), K Nearest Neighbours (K-NN) and
Random Forest (RF), and in terms of the most used deep learning techniques, we found Long-Term
Memory (LSTM) and transformers such as BERT. An important finding in the papers reviewed was
that the use of machine learning techniques has prevailed over those of deep learning. Regarding
the place of origin of the research carried out, we found that 64% of the works belong to studies
carried out in English-speaking countries, 8% in Portuguese and 28% in other languages (such as
German, Chinese, Turkish, Spanish, etc.). Very few works of this type have been carried out in
Spanish-speaking countries. The classification criteria of the works have been based, on the one hand,
on the identification of the classifiers used to predict situations (or events with legal interference) or
judicial decisions and, on the other hand, on the application of classifiers to the phenomena regulated
by the different branches of law: criminal, constitutional, human rights, administrative, intellectual
property, family law, tax law and others. The corpus size analyzed in the reviewed works reached
100,000 documents in 2020. Finally, another important finding lies in the accuracy of these predictive
techniques, reaching predictions of over 60% in different branches of law.
Cómo citar
Alcántara Francia, O.A., Nunez-del-Prado, M. & Alatrista-Salas, H. (2022). Survey of Text Mining Techniques Applied to Judicial Decisions Prediction. Applied Sciences, 12(20). https://doi.org/10.3390/ app122010200Editor
Multidisciplinary Digital Publishing Institute (MDPI)Temas
Revista
Applied SciencesISSN
2076-3417Nota
Indexado en Scopus
Coleccion(es)
- Derecho [42]
El ítem tiene asociados los siguientes ficheros de licencia: