Artificial Intelligence and Law

, Volume 19, Issue 4, pp 291–331 | Cite as

A framework for the extraction and modeling of fact-finding reasoning from legal decisions: lessons from the Vaccine/Injury Project Corpus

  • Vern R. WalkerEmail author
  • Nathaniel Carie
  • Courtney C. DeWitt
  • Eric Lesh


This article describes the Vaccine/Injury Project Corpus, a collection of legal decisions awarding or denying compensation for health injuries allegedly due to vaccinations, together with models of the logical structure of the reasoning of the factfinders in those cases. This unique corpus provides useful data for formal and informal logic theory, for natural-language research in linguistics, and for artificial intelligence research. More importantly, the article discusses lessons learned from developing protocols for manually extracting the logical structure and generating the logic models. It identifies sub-tasks in the extraction process, discusses challenges to automation, and provides insights into possible solutions for automation. In particular, the framework and strategies developed here, together with the corpus data, should allow “top–down” and contextual approaches to automation, which can supplement “bottom-up” linguistic approaches. Illustrations throughout the article use examples drawn from the Corpus.


Argumentation mining Automation Legal evidence Legal rule Logic schema Vaccines 



The authors gratefully acknowledge the work on the Vaccine/Injury Project of their colleagues at the Law, Logic and Technology Research Laboratory: Irnande Altema, Orly Bertel, Chan Hee Park, Sonsy Rajan, Rida Siddiqui, Shiv Vydyula, and Elena Weinstein.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Vern R. Walker
    • 1
    Email author
  • Nathaniel Carie
    • 1
  • Courtney C. DeWitt
    • 1
  • Eric Lesh
    • 1
  1. 1.Research Laboratory for Law, Logic and TechnologyHofstra University School of LawHempsteadUSA

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