Jurisprudence search in Colombia based on natural language processing (NLP) and Linked Data

Abstract

Objective— To develop a search model for judicial decisions supported by natural language processing that allows analyzing the text of jurisprudential sentences. Additionally, link-data is used to take advantage of the interrelation of content in related court decisions and improve search processes. Methodology— The search model was built in two phases: the first is the training phase to generate the models required to create an index, and second, a search phase where the user enters a search string that is used to find the documents (court decisions) more related to the search. The model was compared with other existing search engines of the Supreme Court of Justice of Colombia. The evaluation was divided into 2 steps. 1) Evaluation of the results obtained in each search, 2) User satisfaction with the results obtained in the searches solution. Results— The developed platform outperforms the existing search system of the court regarding user satisfaction and precision. Conclusions— The designed model for judicial sentences based on Natural Language Processing (NLP) and linked contributes to improving the user experience and the precision of the jurisprudence search.