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

Authors

Vern R. Walker, Maurice A. Deane School of Law at Hofstra UniversityFollow
Nathaniel Carie
Courtney C. Dewitt
Eric Lesh

Document Type

Article

Publication Title

Artificial Intelligence and Law

Publication Date

2011

DOI

10.1007/s10506-011-9115-2

Abstract

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.

Comments

The final publication is available at Springer Nature via https://doi.org/10.1007/s10506-011-9115-2.

Recommended Citation

Vern R. Walker, Nathaniel Carie, Courtney C. Dewitt, and Eric Lesh, A framework for the extraction and modeling of fact-finding reasoning from legal decisions: lessons from the Vaccine/Injury Project Corpus, 19 291 (2011)
Available at: https://scholarlycommons.law.hofstra.edu/faculty_scholarship/272