Abstract
This paper presents work in progress to design and test a new model for indexing judicial decisions using facets. We have created a prototype database with a sample of 2 500 judicial decisions in the fields of administrative law, labor law and the law of obligations. The decisions are indexed using four facets (Person, Action, Thing, Context) and two classes (Legal category, Remedies and sanctions). We will test the prototype to compare its performance with that of a standard database. It is hoped that our model will support the full or partial automation of legal indexing and provide a structure for AI applications in the legal domain.
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Acknowledgments
This research program is funded by the Social Sciences and Humanities Research Council of Canada (2015–2020). We are grateful for the research assistance of Christophe Achdjian, Dominique Boulanger, Cécile Gaiffe, Marjorie Gauchier, Joanie Gosselin-Lepage, Béatrice Lecomte, Erin Sandberg, Forrest Wakarchuk and Iryna Zazulya. Thanks to Alexandre Fortier, our expert librarian, David Lesieur, our programmer and Charles Tremblay-Potvin, our project coordinator.
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Cumyn, M., Hudon, M., Mas, S., Reiner, G. (2018). Towards a New Approach to Legal Indexing Using Facets. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_84
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DOI: https://doi.org/10.1007/978-3-319-92058-0_84
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