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From Case Law to Ratio Decidendi

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New Frontiers in Artificial Intelligence (JSAI-isAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10838))

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Abstract

This paper is concerned with the task of automatically identifying legally binding principles, known as ratio decidendi or just ratio, from transcripts of court judgements, also called case law or just cases. After briefly reviewing the relevant definitions and previous work in the area, we present a novel system for automatically extracting ratio from cases using a combination of natural language processing and machine learning. Our approach is based on the hypothesis that the ratio of a given case can be reliably obtained by identifying statements of legal principles in paragraphs that are cited by subsequent cases. Our method differs from related recent work by extracting principles from the text of the cited paragraphs (in the given case) as opposed to the text of the citing paragraphs (in a subsequent case). We conduct our own independent small-scale annotation study which reveals that this seemingly subtle shift of focus substantially increases reliability of finding the ratio. Then, by building on previous work in the automatic detection of legal principles and cross citations, we present a fully automated system that successfully identifies the ratio (in our study) with an accuracy of 72%.

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Notes

  1. 1.

    Westlaw UK, Online legal research from Sweet & Maxwell, http://westlaw.co.uk.

  2. 2.

    Unfortunately, we could not access the data corpus used by Saravanan et al. since the links in their paper are out of service and we have not received a reply to our email asking for additional information. Thus, we could only inspect the examples in the paper itself.

  3. 3.

    \(\kappa \) is the predominant agreement measure that corrects raw agreement P(A) for agreement by chance P(E) [3, 8]:

    $$\begin{aligned} \textstyle \kappa = \frac{P(A) - P(E)}{1-P(E)}. \end{aligned}$$

References

  1. Adedjouma, M., Sabetzadeh, M., Briand, L.C.: Automated detection and resolution of legal cross references: approach and a study of Luxembourg’s legislation. In: 2014 IEEE 22nd International Requirements Engineering Conference (RE), pp. 63–72, August 2014

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  8. Shulayeva, O., Siddharthan, A., Wyner, A.: Recognizing cited facts and principles in legal judgements. Artif. Intell. Law 25(1), 107–126 (2017). https://doi.org/10.1007/s10506-017-9197-6

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK

    Josef Valvoda & Oliver Ray

Authors
  1. Josef Valvoda
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  2. Oliver Ray
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Corresponding author

Correspondence to Josef Valvoda .

Editor information

Editors and Affiliations

  1. Chiba University, Chiba, Japan

    Sachiyo Arai

  2. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

  5. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  6. Fujitsu Laboratories Limited, Kanagawa, Japan

    Yuiko Ohta

Appendix

Appendix

Table 1. Per category and aggregated statistics for the original Shulayeva et al.’s principle and fact classifier trained on Gold Standard corpus.
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Table 2. Per category and aggregated statistics for Shulayeva et al.’s classifier trained on New corpus for extraction of principles only.
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Table 3. Distribution of ratio and obiter between cited and not cited paragraphs containing principle.
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Table 4. Distribution of ratio and obiter between citing and not citing paragraphs containing principle.
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Table 5. Per category and aggregated statistics for cited paragraph classifier.
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Table 6. Per category and aggregated statistics for Ratio Decidendi classifier.
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Valvoda, J., Ray, O. (2018). From Case Law to Ratio Decidendi. In: Arai, S., Kojima, K., Mineshima, K., Bekki, D., Satoh, K., Ohta, Y. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2017. Lecture Notes in Computer Science(), vol 10838. Springer, Cham. https://doi.org/10.1007/978-3-319-93794-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-93794-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93793-9

  • Online ISBN: 978-3-319-93794-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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