Identifying delegation and constraints in legislative texts: A computational method applied to the European Union

We introduce a computational method for identifying delegating and constraining provisions in European Union (EU) laws. Leveraging the syntactic structures employed by legislators, we developed a set of extraction rules applied through a custom-built computational linguistics pipeline. We run through the pipeline more than 600,000 legal sentences that we extracted from 9319 laws adopted between 1958 and 2019. The application performs very well vis-á-vis human annotation and outperforms transformer models. The produced patterns of authority delegation and constraint resonate with our knowledge of the policymaking and history of the EU. Our approach provides valuable insights for designing transparent and adaptable rule-based computational linguistic methods of legal text analysis. We also release the comprehensively annotated dataset and the fine-tuned transformer models developed for this task.

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Publication Details

Journal
European Union Politics
Published
2026-03-04
DOI
https://doi.org/10.1177/14651165261423778
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
0.00
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article

Identifying delegation and constraints in legislative texts: A computational method applied to the European Union

Marta Migliorati, Fabio Franchino, Giovanni Pagano, Valerio Vignoli
European Union Politics
Artificial Intelligence in Law
article

Identifying delegation and constraints in legislative texts: A computational method applied to the European Union

Marta Migliorati, Fabio Franchino, Giovanni Pagano, Valerio Vignoli
article en

Abstract

We introduce a computational method for identifying delegating and constraining provisions in European Union (EU) laws. Leveraging the syntactic structures employed by legislators, we developed a set of extraction rules applied through a custom-built computational linguistics pipeline. We run through the pipeline more than 600,000 legal sentences that we extracted from 9319 laws adopted between 1958 and 2019. The application performs very well vis-á-vis human annotation and outperforms transformer models. The produced patterns of authority delegation and constraint resonate with our knowledge of the policymaking and history of the EU. Our approach provides valuable insights for designing transparent and adaptable rule-based computational linguistic methods of legal text analysis. We also release the comprehensively annotated dataset and the fine-tuned transformer models developed for this task.

European Union Politics
University of Siena (IT), University of Milan (IT), ETH Zurich (CH)
Peace, Justice and strong institutions
Openalex Percentile: Top 27%
Artificial Intelligence in Law
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