Exploiting Properties of Legislative Texts to Improve Classification Accuracy

Authors:
Rob Opsomer;Geert De Meyer;Chris Cornelis;Greet Van Eetvelde
Affiliations:
Ghent University, Dep. of Civil Techniques, Environmental and Spatial Management;Flemish Institute for Technological Research;Ghent University, Dep. of Appl. Math. & CS, Computational Web Intelligence;Ghent University, Dep. of Civil Techniques, Environmental and Spatial Management
Venue:
Proceedings of the 2009 conference on Legal Knowledge and Information Systems: JURIX 2009: The Twenty-Second Annual Conference
Year:
2009

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Abstract

Organizing legislative texts into a hierarchy of legal topics enhances the access to legislation. Manually placing every part of new legislative texts in the correct place of the hierarchy, however, is expensive and slow, and therefore naturally calls for automation. In this paper, we assess the ability of machine learning methods to develop a model that automatically classifies legislative texts in a legal topic hierarchy. It is investigated whether such methods can generalize across different codes. In the classification process, the specific properties of legislative documents are exploited. Both the hierarchical structure of legal codes and references within the legal document collection are taken into account. We argue for a closer cooperation between legal and machine learning experts as the main direction of future work.