Extracting and classifying exceptional COVID-19 measures from multilingual legal texts: The merits and limitations of automated approaches

Clara Egger*, Tommaso Caselli, Georgios Tziafas, Eugénie de Saint Phalle, Wietse de Vries

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

This paper contributes to ongoing scholarly debates on the merits and limitations of computational legal text analysis by reflecting on the results of a research project documenting exceptional COVID-19 management measures in Europe. The variety of exceptional measures adopted in countries characterized by different legal systems and natural languages, as well as the rapid evolution of such measures, pose considerable challenges to manual textual analysis methods traditionally used in the social sciences. To address these challenges, we develop a supervised classifier to support the manual coding of exceptional policies by a multinational team of human coders. After presenting the results of various natural language processing (NLP) experiments, we show that human-in-the-loop approaches to computational text analysis outperform unsupervised approaches in accurately extracting policy events from legal texts. We draw lessons from our experience to ensure the successful integration of NLP methods into social science research agendas.

Original languageEnglish
Pages (from-to)704-723
Number of pages20
JournalRegulation and Governance
Volume18
Issue number3
DOIs
Publication statusE-pub ahead of print - 2 Oct 2023

Bibliographical note

Funding Information:
This work is part of the EXCEPTIUS project, financed by Dutch Organization for Health Development and Research (ZonMw, project number: 10430032010026).

Publisher Copyright:
© 2023 The Authors. Regulation & Governance published by John Wiley & Sons Australia, Ltd.

Research programs

  • ESSB PA

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