Clara Egger, Tommaso Caselli, Georgios Tziafas, Eugénie de Saint Phalle, Wietse de Vries
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引用次数: 0
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.
期刊介绍:
Regulation & Governance serves as the leading platform for the study of regulation and governance by political scientists, lawyers, sociologists, historians, criminologists, psychologists, anthropologists, economists and others. Research on regulation and governance, once fragmented across various disciplines and subject areas, has emerged at the cutting edge of paradigmatic change in the social sciences. Through the peer-reviewed journal Regulation & Governance, we seek to advance discussions between various disciplines about regulation and governance, promote the development of new theoretical and empirical understanding, and serve the growing needs of practitioners for a useful academic reference.