Text clustering applied to data augmentation in legal contexts
Lucas José Gonçalves Freitas Thaís Rodrigues Guilherme Rodrigues Pamella Edokawa Ariane Farias
Data analysis and machine learning are of preeminent importance in the legal
domain, especially in tasks like clustering and text classification. In this
study, we harnessed the power of natural language processing tools to enhance
datasets meticulously curated by experts. This process significantly improved
the classification workflow for legal texts using machine learning techniques.
We considered the Sustainable Development Goals (SDGs) data from the United
Nations 2030 Agenda as a practical case study. Data augmentation
clustering-based strategy led to remarkable enhancements in the accuracy and
sensitivity metrics of classification models. For certain SDGs within the 2030
Agenda, we observed performance gains of over 15%. In some cases, the example
base expanded by a noteworthy factor of 5. When dealing with unclassified legal
texts, data augmentation strategies centered around clustering prove to be
highly effective. They provide a valuable means to expand the existing
knowledge base without the need for labor-intensive manual classification
efforts.