[BibTeX] [RIS]
Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain
Type of publication: Incollection
Citation: jf:SemanticLaw
Booktitle: Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language
Edition: 1
Series: Lecture Notes in Artificial Intelligence
Volume: 6036
Year: 2010
Month: May
Pages: 192-215
Publisher: Springer-Verlag
Note: accompanying EUR-Lex dataset available at \url{http://www.ke.tu-darmstadt.de/resources/eurlex}
ISBN: 978-3-642-12836-3
URL: http://www.ke.tu-darmstadt.de/publications/papers/loza10eurlex.pdf
DOI: 10.1007/978-3-642-12837-0_11
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: EUR-Lex Database, learning by pairwise comparison, Legal Documents, multilabel classification, Text Classification
Authors Loza Mencía, Eneldo
Fürnkranz, Johannes
Editors Francesconi, Enrico
Montemagni, Simonetta
Peters, Wim
Tiscornia, Daniela
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