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Exploiting properties of legislative texts to improve classification accuracy

(2009) FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS. 205(Proceedings of the 22nd Annual conference on Legal Knowledge and Information Systems). p.136-145
Author
Organization
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.
Keywords
legislative documents, law, legal classification, machine learning, Text classification

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MLA
Opsomer, Rob et al. “Exploiting Properties of Legislative Texts to Improve Classification Accuracy.” Frontiers in Artificial Intelligence and Applications. Ed. Guido Governatori. Vol. 205. Amsterdam, The Netherlands: IOS Press, 2009. 136–145. Print.
APA
Opsomer, R., De Meyer, G., Cornelis, C., & Van Eetvelde, G. (2009). Exploiting properties of legislative texts to improve classification accuracy. In G. Governatori (Ed.), FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS (Vol. 205, pp. 136–145). Presented at the 22nd Annual conference on Legal Knowledge and Information Systems (Jurix 2009), Amsterdam, The Netherlands: IOS Press.
Chicago author-date
Opsomer, Rob, Geert De Meyer, Chris Cornelis, and Greet Van Eetvelde. 2009. “Exploiting Properties of Legislative Texts to Improve Classification Accuracy.” In Frontiers in Artificial Intelligence and Applications, ed. Guido Governatori, 205:136–145. Amsterdam, The Netherlands: IOS Press.
Chicago author-date (all authors)
Opsomer, Rob, Geert De Meyer, Chris Cornelis, and Greet Van Eetvelde. 2009. “Exploiting Properties of Legislative Texts to Improve Classification Accuracy.” In Frontiers in Artificial Intelligence and Applications, ed. Guido Governatori, 205:136–145. Amsterdam, The Netherlands: IOS Press.
Vancouver
1.
Opsomer R, De Meyer G, Cornelis C, Van Eetvelde G. Exploiting properties of legislative texts to improve classification accuracy. In: Governatori G, editor. FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS. Amsterdam, The Netherlands: IOS Press; 2009. p. 136–45.
IEEE
[1]
R. Opsomer, G. De Meyer, C. Cornelis, and G. Van Eetvelde, “Exploiting properties of legislative texts to improve classification accuracy,” in FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, Amsterdam, The Netherlands, 2009, vol. 205, no. Proceedings of the 22nd Annual conference on Legal Knowledge and Information Systems, pp. 136–145.
@inproceedings{825719,
  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.},
  author       = {Opsomer, Rob and De Meyer, Geert and Cornelis, Chris and Van Eetvelde, Greet},
  booktitle    = {FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS},
  editor       = {Governatori, Guido},
  isbn         = {9781607500827},
  issn         = {0922-6389},
  keywords     = {legislative documents,law,legal classification,machine learning,Text classification},
  language     = {eng},
  location     = {Amsterdam, The Netherlands},
  number       = {Proceedings of the 22nd Annual conference on Legal Knowledge and Information Systems},
  pages        = {136--145},
  publisher    = {IOS Press},
  title        = {Exploiting properties of legislative texts to improve classification accuracy},
  url          = {http://dx.doi.org/10.3233/978-1-60750-082-7-136},
  volume       = {205},
  year         = {2009},
}

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