ID  - jf:SemanticLaw
T1  - Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain
A1  - Loza Mencía, Eneldo
A1  - Fürnkranz, Johannes
ED  - Francesconi, Enrico
ED  - Montemagni, Simonetta
ED  - Peters, Wim
ED  - Tiscornia, Daniela
TI  - Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language
T3  - Lecture Notes in Artificial Intelligence
Y1  - 2010
VL  - 6036
SP  - 192
EP  - 215
U1  - Edition: 1
PB  - Springer-Verlag
SN  - 978-3-642-12836-3
N1  - accompanying EUR-Lex dataset available at \url{http://www.ke.tu-darmstadt.de/resources/eurlex}
UR  - http://www.ke.tu-darmstadt.de/publications/papers/loza10eurlex.pdf
M2  - doi: 10.1007/978-3-642-12837-0_11
KW  - EUR-Lex Database
KW  - learning by pairwise comparison
KW  - Legal Documents
KW  - multilabel classification
KW  - Text Classification
N2  - 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.
ER  -