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Data-centric and logic-based models for automated legal problem solving

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Abstract

Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges of formalizing legal rules and matching legal predicates with the language of ordinary parlance by exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each.

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Notes

  1. http://tech.law.stanford.edu/ [Accessed: 12 November 2016].

  2. This many-to-many mapping is the result, at least in part, of the many pragmatic functions of natural languages beyond simply expressing propositional content.

  3. For an example of an attempt to scale up formalization of legislation, see (van Engers and Nijssen 2014).

  4. While there have been experiments in automating the extraction of predefined factors from curated collections, e.g., Brüninghaus and Ashley (2001), this work depended on a pre-existing set of factors manually engineered for each domain.

  5. For a discussion of other approaches to open texture, see Bench-Capon and Visser (1997).

  6. https://lexmachina.com/ [Accessed: 27 November 2016].

  7. https://lexpredict.com/ [Accessed: 29 November 2016].

  8. https://premonition.ai/ [Accessed: 27 November 2016].

  9. https://www.legalrobot.com/ [Accessed: 2 December 2016].

  10. https://casetext.com/ [Accessed: 2 December 2016].

  11. http://www.cis.upenn.edu/~treebank/home.html. The Penn Treebank Project annotates naturally-occurring text for linguistic structure.

  12. http://citris-uc.org/ctrs-groups/project/proactive-legal-information-retrieval-and-filtering/.

  13. https://lexmachina.com/ [Accessed: 27 November 2016].

  14. See Prakken (2005) for a review of the history of argumentation schemes in automated legal reasoning.

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The MITRE Corporation is a not-for-profit Federally Funded Research and Development Center chartered in the public interest. This document is approved for Public Release, Distribution Unlimited, Case Number 16-4565 ©2016 The MITRE Corporation. All rights reserved.

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Branting, L.K. Data-centric and logic-based models for automated legal problem solving. Artif Intell Law 25, 5–27 (2017). https://doi.org/10.1007/s10506-017-9193-x

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