Application of Topic Models to Judgments from Public Procurement Domain

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dc.contributor.authorŁopuszyński, Michał
dc.contributor.organizationInterdisciplinary Centre for Mathematical and Computational Modelling, University of Warsawpl_PL
dc.date.accessioned2014-12-23T08:00:57Z
dc.date.available2014-12-23T08:00:57Z
dc.date.issued2014-12
dc.description.abstractIn 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.pl_PL
dc.description.epersonMichał Łopuszyński
dc.identifier.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, 2014pl_PL
dc.identifier.urihttp://dx.doi.org/10.3233/978-1-61499-468-8-131
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/5931
dc.publisherIOSPresspl_PL
dc.relation.ispartofseriesFrontiers in Artificial Intelligence and Applications;271
dc.rightsUznanie autorstwa 3.0 Polska
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/pl/
dc.subjectlegal textspl_PL
dc.subjectlatent dirichlet allocationpl_PL
dc.subjecttopic modelspl_PL
dc.titleApplication of Topic Models to Judgments from Public Procurement Domainpl_PL
dc.typeconferencePaperpl_PL
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