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Integrated access to legal literature through automated semantic classification

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Abstract

Access to legal information and, in particular, to legal literature is examined for the creation of a search and retrieval system for Italian legal literature. The design and implementation of services such as integrated access to a wide range of resources are described, with a particular focus on the importance of exploiting metadata assigned to disparate legal material. The integration of structured repositories and Web documents is the main purpose of the system: it is constructed on the basis of a federation system with service provider functions, aiming at creating a centralized index of legal resources. The index is based on a uniform metadata view created for structured data by means of the OAI approach and for Web documents by a machine learning approach, which, in this paper, has been assessed as regards document classification. Semantic searching is a major requirement for legal literature users and a solution based on the exploitation of Dublin Core metadata, as well as the use of legal ontologies and related terms prepared for accessing indexed articles have been implemented.

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Notes

  1. Legal literature consists in legal intellectual outputs published in monographs, journal articles, manuals, grey literature, proceedings, etc.

  2. Legislation on the Net http://www.normeinrete.it.

  3. On-line Public Access Catalogues.

  4. The DoGi database (http://nir.ittig.cnr.it/dogiswish/Index.htm), is, in the Italian legal landscape, one of the most precious sources for legal literature research. It is a database created in 1970, offering abstracts of articles published in the most important legal periodicals (more than 250). Its main goal is to provide law scholars and professionals with exhaustive and updated information as found in Italian law reviews.

  5. Currently a study of a publisher metadata format is under analysis, therefore the related DC mapping is not described in this paper.

  6. Mapping Dublin Core/UNIMARC is based on tables prepared by ICCU, Rome: http://www.iccu.sbn.it/Edubluni.htm.

  7. Open Archives Initiative (http://www.openarchives.org/OAI/openarchivesprotocol.htm).

  8. TEL—The European Library (http://www.europeanlibrary.org).

  9. CYCLADES—An Open Collaborative Virtual Archive Environment (http://www.ercim.org/cyclades/).

  10. TORII—The Digital Research Community (http://library.cern.ch/HEPLW/4/papers/4/).

  11. ARC developed by Digital Library Group, Old Dominion University.

  12. http://www.openarchives.org.

  13. Most of which is summarized at http://www.lub.lu.se/tk/metadata/dctoollist.html.

  14. Such classes, organized in a single-tier set only, have been chosen to test the approach. Possible extensions or hierarchical organization of the classes can be approached respectively by re-training the classifiers according to the new set of classes or using a set of classifiers hierarchically organized as classes are organised.

  15. We used the MSVM implementation at http://www.csie.ntu.edu.tw/~cjlin/bsvm/index.html.

  16. Swish-e, Simple Web Indexing System for Humans—Enhanced (http://swish-e.org).

  17. see http://nir.ittig.cnr.it/dogiswish/consistenze/class2000Eng.htm.

  18. But similar arguments can be provided for the MBDQ modality.

References

  • Apps A (2003) A journal article bibliographic citation Dublin core structured value. http://epub.mimas.ac.uk/DC/citdcsv.html Retrieved 2 May 2003

  • Apté C, Damerau F, Weiss S (1994) Automated learning of decision rules for text categorization. ACM Trans Inf Syst 12(3):233–251

    Article  Google Scholar 

  • Biagioli C, Francesconi E, Passerini A, Montemagni S, Soria C (2005) Automatic semantics extraction in law documents. In: Proceedings of international conference on artificial intelligence and law, pp 133–139

  • Buckley C, Salton G (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523

    Article  Google Scholar 

  • Burges C (1998) A tutorial on support vector machines for pattern recognition. In: Data mining and knowledge discovery, vol 2. Kluwer Academic Publishers, Boston

  • Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:1–25

    Google Scholar 

  • Crammer K, Singer Y (2002) On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res 2:265–292

    Article  MATH  Google Scholar 

  • Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: CIKM ’98: proceedings of the seventh international conference on Information and knowledge management, pp 148–155

  • Francesconi E, Peruginelli G (2004) Opening the legal literature portal to multilingual access. In: Proceedings of the Dublin core conference, pp 37–44

  • Francesconi E, Passerini A (2007) Automatic classification of provisions in legislative texts. Int J Artif Intell Law 15(1):1–17

    Article  Google Scholar 

  • Greenberg JWR (2002) Semantic Web construction: an inquiry of authors’ views on collaborative metadata generation. In: Proceedings of the international conference on Dublin core and metadata for e-communities, pp 45–52

  • Hachey B, Grover C (2005) Automatic legal text summarisation: experiments with summary structuring. In: Proceedings of international conference on artificial intelligence and law, pp 75–84

  • Hsu C-W, Lin C-J (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Article  Google Scholar 

  • Joachims T (1997) A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: Proceedings of the fourteenth international conference on machine learning, Morgan Kaufmann Publishers Inc., San Francisco, US, pp 143–151

  • McCallum A, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr J 3:127–163

    Google Scholar 

  • Moens M-F (2005) Combining structured and unstructured information in a retrieval model for accessing legislation. In: Proceedings of international conference on artificial intelligence and law, pp 141–145

  • Quinlan J (1986) Inductive learning of decision trees. Mach Learn 1:81–106

    Google Scholar 

  • Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

    Article  Google Scholar 

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  • Yang Y, Pedersen J (1997) A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., pp 412–420

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Acknowledgements

Special thanks go to Dr. Anna Archi, senior researcher at ITTIG-CNR, who dedicated her research work to services for retrieving legal literature.

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  1. Institute of Legal Theory and Techniques, Italian National Research Council (ITTIG-CNR), Florence, Italy

    E. Francesconi & G. Peruginelli

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  1. E. Francesconi
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  2. G. Peruginelli
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Correspondence to E. Francesconi.

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Francesconi, E., Peruginelli, G. Integrated access to legal literature through automated semantic classification. Artif Intell Law 17, 31–49 (2009). https://doi.org/10.1007/s10506-008-9072-6

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