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Exploiting EuroVoc’s Hierarchical Structure for Classifying Legal Documents

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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11877)

Abstract

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.

Keywords

Document classification EuroVoc Eur-Lex Legal domain Word embeddings Deep learning 
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Notes

Acknowledgment

The research leading to this work was partly funded by the Federal Ministry of Digital and Economic Affairs of the Republic of Austria and the Jubliaeumsfonds der Stadt Wien. Gerhard Wohlgenannt’s work was supported by the Government of the Russian Federation (Grant 074-U01) through the ITMO Fellowship and Professorship Program.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.Complexity Science HubViennaAustria
  3. 3.ITMO UniversitySt. PetersburgRussia

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