This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.
This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Kevin Ashley is a Professor of Law and Intelligent Systems at the University of Pittsburgh, Senior Scientist, Learning Research and Development Center, and Adjunct Professor of Computer Science. He received a B.A. from Princeton University, New Jersey, a J.D. from Harvard Law School, Massachusetts and a Ph.D. in computer science from the University of Massachusetts. A visiting scientist at the IBM Thomas J. Watson Research Center, New York, NSF Presidential Young Investigator and Fellow of the American Association for Artificial Intelligence, he is co-Editor-in-Chief of Artificial Intelligence and Law and teaches in the University of Bologna Erasmus Mundus doctoral program in Law, Science and Technology.
Inhaltsangabe
Part I. Computational Models of Legal Reasoning: 1. Introducing AI and Law and its role in future legal practice 2. Modeling statutory reasoning 3. Modeling case-based legal reasoning 4. Models for predicting legal outcomes 5. Computational models of legal argument Part II. Legal Text Analytics: 6. Representing legal concepts in ontologies and type systems 7. Making legal informational retrieval smarter 8. Machine learning with legal texts 9. Extracting information from statutory and regulatory texts 10. Extracting argument-related information from legal case texts Part III. Connecting Computational Reasoning Models and Legal Texts: 11. Conceptual legal information retrieval for cognitive computing 12. Cognitive computing legal apps.
Part I. Computational Models of Legal Reasoning: 1. Introducing AI and Law and its role in future legal practice 2. Modeling statutory reasoning 3. Modeling case-based legal reasoning 4. Models for predicting legal outcomes 5. Computational models of legal argument Part II. Legal Text Analytics: 6. Representing legal concepts in ontologies and type systems 7. Making legal informational retrieval smarter 8. Machine learning with legal texts 9. Extracting information from statutory and regulatory texts 10. Extracting argument-related information from legal case texts Part III. Connecting Computational Reasoning Models and Legal Texts: 11. Conceptual legal information retrieval for cognitive computing 12. Cognitive computing legal apps.
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