Application of Topic Models to Judgments from Public Procurement Domain

In this work, automatic analysis of themes contained in a large corpora of judgments from public procurement domain is performed. The employed technique is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed, to use LDA in conjunction with recently developed method of unsupervised keyword extraction. Such an approach improves the interpretability of the automatically obtained topics and allows for better computational performance. The described analysis illustrates a potential of the method in detecting recurring themes and discovering temporal trends in lodged contract appeals. These results may be in future applied to improve information retrieval from repositories of legal texts or as auxiliary material for legal analyses carried out by human experts.