On top of topics: Leveraging topic modeling to study the dynamic case-law of international courts
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Legal scholars study international courts by analyzing only a fraction of available material, which leaves doubts as to whether their accounts correctly capture the dynamics of international law. In this paper we use dynamic topic modeling, a family of unsupervised machine learning techniques, to gauge the shifts in the content of the case-law of international courts over longer time spans. Our results indicate that dynamic topic modeling is a powerful and reliable tool to systematically and accurately track legal change over time and enhance our understanding of courts and their influence on the law.
Original language | English |
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Title of host publication | Legal Knowledge and Information Systems - JURIX 2016 : The 29th Annual Conference |
Number of pages | 6 |
Publisher | IMIA and IOS Press |
Publication date | 1 Jan 2016 |
Pages | 161-166 |
ISBN (Electronic) | 9781614997252 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Event | 29th International Conference on Legal Knowledge and Information Systems, JURIX 2016 - Nice, France Duration: 14 Dec 2016 → 16 Dec 2016 |
Conference
Conference | 29th International Conference on Legal Knowledge and Information Systems, JURIX 2016 |
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Land | France |
By | Nice |
Periode | 14/12/2016 → 16/12/2016 |
Series | Frontiers in Artificial Intelligence and Applications |
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Volume | 294 |
ISSN | 0922-6389 |
- Case-law, Court of justice of the EU, European court of human rights, Machine learning, Topic modeling
Research areas
ID: 203177889