Application of Topic Models to Judgments from Public Procurement Domain

Abstract
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.
Description
Keywords
legal texts  latent dirichlet allocation  topic models
Citation
"Legal Knowledge and Information Systems, JURIX 2014: The Twenty-Seventh Annual Conference", series Frontiers in Artificial Intelligence and Applications, Volume 271, edited by Rinke Hoekstra, IOSPress, 2014