Giving every case its (legal) due: The contribution of citation networks and text similarity techniques to legal studies of European Union law
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
In this article we propose a novel methodology, which uses text similarity techniques to infer precise citations from the judgments of the Court of Justice of the European Union (CJEU), including their content. We construct a complete network of citations to judgments on the level of singular text units or paragraphs. By contrast to previous literature, which takes into account only explicit citations of entire judgments, we also infer implicit citations, meaning the repetitions of legal arguments stemming from past judgments without explicit reference. On this basis we can differentiate between different categories and modes of citations. The latter is crucial for assessing the actual legal importance of judgments in the citation network. Our study is an important methodological step forward in integrating citation network analysis into legal studies, which significantly enhances our understanding of European Union law and the decision making of the CJEU.
Original language | English |
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Title of host publication | Legal Knowledge and Information Systems - JURIX 2017 : The 30th Annual Conference |
Number of pages | 10 |
Publisher | IMIA and IOS Press |
Publication date | 1 Jan 2017 |
Pages | 59-68 |
ISBN (Electronic) | 9781614998372 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Event | 30th International Conference on Legal Knowledge and Information Systems, JURIX 2017 - Luxembourg, Luxembourg Duration: 13 Dec 2017 → 15 Dec 2017 |
Conference
Conference | 30th International Conference on Legal Knowledge and Information Systems, JURIX 2017 |
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Land | Luxembourg |
By | Luxembourg |
Periode | 13/12/2017 → 15/12/2017 |
Series | Frontiers in Artificial Intelligence and Applications |
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Volume | 302 |
ISSN | 0922-6389 |
- Citation networks, CJEU, Network analysis, Text similarity
Research areas
ID: 203178146