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      Application of Topic Models to Judgments from Public Procurement Domain

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          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.

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          Journal
          16 December 2014
          Article
          10.3233/978-1-61499-468-8-131
          1412.5212
          48a650c9-3d00-4f17-a9ef-41b1d34d4a2e

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          "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
          cs.CL

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