Exploiting EuroVoc’s Hierarchical Structure for Classifying Legal Documents

Erwin Filtz, Sabrina Kirrane, Axel Polleres, Gerhard Wohlgenannt

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


Multi-label document classification is a challenging problem because of the potentially huge number of classes. Furthermore, real-world datasets often exhibit a strongly varying number of labels per document, and a power-law distribution of those class labels. Multi-label classification of legal documents is additionally complicated by long document texts and domain-specific use of language. In this paper we use different approaches to compare the performance of text classification algorithms on existing datasets and corpora of legal documents, and contrast the results of our experiments with results on general-purpose multi-label text classification datasets. Moreover, for the EUR-Lex legal datasets, we show that exploiting the hierarchy of the EuroVoc thesaurus helps to improve classification performance by reducing the number of potential classes while retaining the informative value of the classification itself.
Original languageEnglish
Title of host publicationOn the Move to Meaningful Internet Systems: OTM 2019 Conferences
Editors Hervé Panetto, Christophe Debruyne, Martin Hepp, Dave Lewis, Claudio Agostino Ardagna, Robert Meersman
Place of PublicationGreece
PublisherLecture Notes in Computer Science
Pages164 - 181
ISBN (Print)978-3-030-33246-4
Publication statusPublished - 2019

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