Predicting citations in Dutch case law with natural language processing
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a system to predict if Dutch court decisions will be cited, helping legal professionals find authoritative case law more easily. Explainable models using metadata performed nearly as well as complex text-based systems.
Area Of Science
- Legal informatics
- Computational law
- Artificial intelligence in law
Background
- Increasing online accessibility of case law presents challenges in identifying relevant legal precedents.
- The planned expansion of online case law publication in the Netherlands will intensify this issue.
- Distinguishing authoritative case law from less influential decisions is crucial for legal research efficiency.
Purpose Of The Study
- To develop a predictive model for identifying cited court decisions, thereby distinguishing authoritative cases.
- To aid legal practitioners and scholars in efficiently navigating large volumes of legal data.
- To reduce the time and effort required for legal case preparation and analysis.
Main Methods
- Predictive modeling to forecast citation of court decisions post-publication.
- Utilizing handcrafted metadata features and full text analysis for model development.
- Evaluating model performance using the Matthews Correlation Coefficient (MCC).
Main Results
- The predictive model showed a strong correlation for the Dutch Supreme Court (MCC = 0.60).
- Performance was less successful for the Council of State (MCC = 0.26) and district courts (MCC = 0.17).
- Explainable models using only metadata features achieved performance comparable to less interpretable text-based models.
Conclusions
- Predictive citation analysis offers a viable method for filtering authoritative legal decisions.
- Metadata-driven, explainable AI models provide a promising approach for legal informatics applications.
- Further refinement is needed to improve prediction accuracy across different court levels.
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