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Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain

  • Eneldo Loza Mencía
  • Johannes Fürnkranz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6036)

Abstract

In this paper we apply multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. For this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks.

Keywords

Text Classification Multilabel Classification Legal Documents EUR-Lex Database Learning by Pairwise Comparison 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eneldo Loza Mencía
    • 1
  • Johannes Fürnkranz
    • 1
  1. 1.Knowledge Engineering GroupTechnische Universität Darmstadt 

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