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.

中文翻译：

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从多语言法律文本中提取特殊的 COVID-19 措施并进行分类：自动化方法的优点和局限性

本文通过反思记录欧洲特殊的 COVID-19 管理措施的研究项目的结果，为正在进行的关于计算法律文本分析的优点和局限性的学术辩论做出了贡献。具有不同法律体系和自然语言特点的国家采取的各种例外措施，以及这些措施的快速演变，对社会科学中传统使用的手工文本分析方法提出了相当大的挑战。为了应对这些挑战，我们开发了一个监督分类器，以支持跨国人类编码团队对特殊策略的手动编码。在展示了各种自然语言处理（NLP）实验的结果后，我们表明，计算文本分析的人机交互方法在从法律文本中准确提取政策事件方面优于无监督方法。我们吸取经验教训，确保 NLP 方法成功融入社会科学研究议程。