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Extracting Proceedings Data from Court Cases with Machine Learning

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  • Bruno Mathis

    (CHROME Laboratory, Nimes University, 5 Rue du Docteur Georges Salan CS 13019, 30021 Nîmes, France
    European Centre of Law & Economics of ESSEC Business School, 3 Av. Bernard Hirsch, 95000 Cergy, France)

Abstract

France is rolling out an open data program for all court cases, but with few metadata attached. Reusers will have to use named-entity recognition (NER) within the text body of the case to extract any value from it. Any court case may include up to 26 variables, or labels, that are related to the proceeding, regardless of the case substance. These labels are from different syntactic types: some of them are rare; others are ubiquitous. This experiment compares different algorithms, namely CRF, SpaCy, Flair and DeLFT, to extract proceedings data and uses the learning model assessment capabilities of Kairntech, an NLP platform. It shows that an NER model can apply to this large and diverse set of labels and extract data of high quality. We achieved an 87.5% F1 measure with Flair trained on more than 27,000 manual annotations. Quality may yet be improved by combining NER models by data type.

Suggested Citation

  • Bruno Mathis, 2022. "Extracting Proceedings Data from Court Cases with Machine Learning," Stats, MDPI, vol. 5(4), pages 1-16, December.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:4:p:79-1320:d:1001711
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    References listed on IDEAS

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    1. Daniel Martin Katz & Michael J Bommarito II & Josh Blackman, 2017. "A general approach for predicting the behavior of the Supreme Court of the United States," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
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