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

Artificial Intelligence and Law 19 (4):291-331 (2011)
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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.

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References found in this work

Argumentation schemes.Douglas Walton, Chris Reed & Fabrizio Macagno - 2008 - New York: Cambridge University Press. Edited by Chris Reed & Fabrizio Macagno.
Argumentation Schemes.Douglas Walton, Christopher Reed & Fabrizio Macagno - 2008 - Cambridge and New York: Cambridge University Press. Edited by Chris Reed & Fabrizio Macagno.
Argumentation schemes for presumptive reasoning.Douglas N. Walton - 1996 - Mahwah, N.J.: L. Erlbaum Associates.
Nomic Probability and the Foundations of Induction.John L. Pollock - 1990 - New York, NY, USA: Oxford University Press.

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