Scaling out to become doctrinal

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

International courts are often prolific and produce a huge amount of decisions per year which makes it extremely difficult both for researchers and practitioners to follow. It would be thus convenient for the legal researchers to be given the ability to get an idea of the topics that are dealt with in the judgments produced by the courts, without having to read through the judgments. This is exactly a use case for topic modeling, however, the volume of data is such that calls for an out-of-core solution. In this paper we are experimenting in this direction by using the data from two major, large international courts.We thus, experiment with topic modeling in Big Data architectures backed by a MapReduce framework. We demonstrate both the feasibility of our approach and the accuracy of the produced topic models that manage to outline very well the development of the subject matters of the courts under study.

Original languageEnglish
Title of host publicationAlgorithmic Aspects of Cloud Computing - 2nd International Workshop,ALGOCLOUD 2016, Revised Selected Papers
Number of pages12
PublisherSpringer Verlag,
Publication date1 Jan 2017
ISBN (Print)9783319570440
Publication statusPublished - 1 Jan 2017
Event2nd International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2016 - Aarhus, Denmark
Duration: 22 Aug 201622 Aug 2016


Conference2nd International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2016
By Aarhus
SponsorDanish National Research Foundation Center
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10230 LNCS

    Research areas

  • Big Data, European Court of Human Rights, European Court of Justice, Latent Dirichlet Allocation, MapReduce

ID: 203175868