Exploring the Challenges and Limitations of Unsupervised Machine Learning Approaches in Legal Concepts Discovery

The utilization of machine learning methods for the analysis and interpretation of legal documents has been growing over the years, yet their potential and limitations remain under-explored. This study aims to address this gap, using unsupervised machine learning techniques to discover legal concepts from a corpus of Spanish legal documents. In addition to striving for optimal results, our research also embarks on an exploration of the challenges and limitations of unsupervised machine learning, investigating its capabilities and limitations in legal text analysis. We demonstrate that even relatively simplistic methodologies can yield noteworthy insights, with the highest identification rate of 70% achieved by Topic Modeling with Latent Dirichlet Allocation (LDA). However, challenges were encountered with the identification of some concepts, suggesting potential improvements in the corpus preprocessing and tokenization stages or the techniques to be used. The findings underscore the potential of unsupervised learning algorithms in legal text analysis, offering an intriguing path for future research. While acknowledging the need for higher accuracy in practical applications, this study emphasizes the remarkable feat achieved and proposes a way forward for a hybrid or adaptable approach.