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. Walker
  • Nathaniel Carie
  • Courtney C. DeWitt
  • Eric Lesh
Article

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.

Keywords

Argumentation mining Automation Legal evidence Legal rule Logic schema Vaccines 

References

  1. Aman AC Jr, Mayton WT (2001) Administrative law, 2nd edn. West Group, St. PaulGoogle Scholar
  2. Anderson T, Twining W (1991) Analysis of evidence: how to do things with facts based on Wigmore’s Science of judicial proof. Northwestern University Press, EvanstonGoogle Scholar
  3. Ashley KD (2009). Ontological requirements for analogical, teleological, and hypothetical legal reasoning. In: Proceedings of 12th international conference on artificial intelligence and law (ICAIL-09). ACM, New York, pp 1–10Google Scholar
  4. Ashley KD, Brüninghaus S (2009) Automatically classifying texts and predicting outcomes. Artif Intell Law 17:125–165CrossRefGoogle Scholar
  5. Ashley KD, Rissland EL (2003) Law, learning and representation. Artif Intell 150:17–58CrossRefMATHMathSciNetGoogle Scholar
  6. Bex FJ, van Koppen PJ, Prakken H (2010) A hybrid theory of arguments, stories and criminal evidence. Artif Intell Law 18:123–152CrossRefGoogle Scholar
  7. Biagioli C, Francesconi E, Passerini A, Montemagni S, Soria C (2005). Automatic semantics extraction in law documents. In: Proceedings of tenth international conference on artificial intelligence and law (ICAIL-05). ACM, New York, pp 133–140Google Scholar
  8. Brachman RJ, Levesque HJ (2004) Knowledge representation and reasoning. Elsevier, AmsterdamGoogle Scholar
  9. Branting LK (2000) Reasoning with rules and precedents: a computational model of legal analysis. Kluwer, DordrechtGoogle Scholar
  10. Brewer S (1996) Exemplary reasoning: semantics, pragmatics, and the rational force of legal argument by analogy. Harv Law Rev 109:923CrossRefGoogle Scholar
  11. Brewka G, Dix J, Konolige K (1997) Nonmonotonic reasoning: an overview. CSLI Publications, StanfordMATHGoogle Scholar
  12. Brüninghaus S, Ashley KD (2005). Generating legal arguments and predictions from case texts. In: Proceedings of 10th international conference on artificial intelligence and law (ICAIL-05). ACM, New York, pp 65–74Google Scholar
  13. Carmines EG, Zeller RA (1979) Reliability and validity assessment. Sage, Newbury ParkGoogle Scholar
  14. Chorley A, Bench-Capon T (2005) An empirical investigation of reasoning with legal cases through theory construction and application. Artif Intell Law 13:323–371CrossRefGoogle Scholar
  15. Gordon TF, Walton D (2009). Legal reasoning with argumentation schemes. In: Proceedings of 12th international conference on artificial intelligence and law (ICAIL-09). ACM, New York, pp 137–146Google Scholar
  16. Kadane JB, Schum DA (1996) A probabilistic analysis of the Sacco and Vanzetti evidence. Wiley, New YorkGoogle Scholar
  17. Kyburg HE Jr, Teng CM (2001) Uncertain inference. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  18. Mochales R, Moens M-F (2008). Study on the structure of argumentation in case law. In: Legal knowledge and information systems (Jurix 2008). IOS Press, pp 11–20Google Scholar
  19. Moens M-F, Boiy E, Palau R, Reed C (2007). Automatic detection of arguments in legal texts. In: Proceedings of 11th international conference on artificial intelligence and law (ICAIL-07). ACM, New York, pp 225–230Google Scholar
  20. Palau RM, Moens M-F (2009) Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of 12th international conference on artificial intelligence and law (ICAIL-09). ACM, New York, pp 98–107Google Scholar
  21. Pollock JL (1990) Nomic probability and the foundations of induction. Oxford University Press, New YorkGoogle Scholar
  22. Prakken H (2004) Analysing reasoning about evidence with formal models of argumentation. Law Probab Risk 3(1):33–50CrossRefGoogle Scholar
  23. Prakken H (2008) Formalising ordinary legal disputes: a case study. Artif Intell Law 16:333–359CrossRefGoogle Scholar
  24. Prakken H, Sartor G (2004) The three faces of defeasibility in the law. Ratio Juris 17(1):118–139CrossRefGoogle Scholar
  25. Prakken H, Reed C, Walton D (2003) Argumentation schemes and generalisations in reasoning about evidence. In: Proceedings of 9th international conference of artificial intelligence and law (ICAIL-03). ACM, New York, pp 32–41Google Scholar
  26. Rissland EL, Ashley KD, Loui RP (2003) AI and law: a fruitful synergy. Artif Intell 150:1–15CrossRefMathSciNetGoogle Scholar
  27. Saravanan M, Ravindran B (2010) Identification of rhetorical roles for segmentation and summarization of a legal judgment. Artif Intell Law 18:45–76CrossRefGoogle Scholar
  28. Schum DA (1994) Evidential foundations of probabilistic reasoning. Wiley, New YorkGoogle Scholar
  29. Toulmin S, Rieke R, Janik A (1984) An introduction to reasoning. Macmillan, New YorkGoogle Scholar
  30. Verheij B (2005) Virtual arguments: on the design of argument assistants for lawyers and other arguers. TMC Asser Press, The HagueGoogle Scholar
  31. Walker VR (2003) Epistemic and non-epistemic aspects of the factfinding process in law. Am Phil Assoc Newsl Law Phil 3(1):132–136Google Scholar
  32. Walker VR (2004) Restoring the individual plaintiff to tort law by rejecting “Junk Logic” about specific causation. Ala Law Rev 56(2):381–481Google Scholar
  33. Walker VR (2007a) A default-logic paradigm for legal fact-finding. Jurimetrics 47:193–243Google Scholar
  34. Walker VR (2007b) Visualizing the dynamics around the rule-evidence interface in legal reasoning. Law Probab Risk 6(1–4):5–22CrossRefGoogle Scholar
  35. Walker VR (2009a) Emergent reasoning structures in law. In: Trajkovski G, Collins SG (eds) Handbook of research on agent-based societies: social and cultural interactions. Information Science Reference, Hershey, pp 305–324CrossRefGoogle Scholar
  36. Walker VR (2009b) Designing factfinding for cross-border healthcare. Opinio Juris in Comparatione 3(1):1–40Google Scholar
  37. Walton DN (1996) Argument schemes for presumptive reasoning. Lawrence Erlbaum, MahwahGoogle Scholar
  38. Walton D, Reed C, Macagno F (2008) Argumentation schemes. Cambridge University Press, CambridgeGoogle Scholar
  39. Wyner A, Peters W (2010) Lexical semantics and expert legal knowledge towards the identification of legal case factors. In: Legal knowledge and information systems (Jurix 2010). IOS PressGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

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

Personalised recommendations