Catboost Algorithm Application in Legal Texts and UN 2030 Agenda

This article evaluates the application of the Catboost algorithm for automatic classification of legal texts in The United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDGs). The task consists of labeling texts from initial petitions and rulings based on identifying topics related to the objectives of the 2030 Agenda, which include sustainable development, quality education, gender equality, preservation of the environment, among other topics of interest to UN member countries. This work aims to help Judicial System employees in case management task, an activity that is manual and repetitive. Since the Catboost algorithm allows joining textual, numerical and categorical features in the same classification model. The proposed approach adds to the classification algorithm traditional metadata about legal processes, such as the Supreme Court Class and Field of Law. The main contributions of this work are: analysis of metadata in machine learning flows and evaluation of the Catboost algorithm for textual classification in legal contexts

Catboost Algorithm Application in Legal Texts and UN 2030 Agenda

This article evaluates the application of the Catboost algorithm for automatic classification of legal texts in The United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDGs). The task consists of labeling texts from initial petitions and rulings based on identifying topics related to the objectives of the 2030 Agenda, which include sustainable development, quality education, gender equality, preservation of the environment, among other topics of interest to UN member countries. This work aims to help Judicial System employees in case management task, an activity that is manual and repetitive. Since the Catboost algorithm allows joining textual, numerical and categorical features in the same classification model. The proposed approach adds to the classification algorithm traditional metadata about legal processes, such as the Supreme Court Class and Field of Law. The main contributions of this work are: analysis of metadata in machine learning flows and evaluation of the Catboost algorithm for textual classification in legal contexts