Masters's Thesis Jieyi Zhang
Title: Automatic Norm Chain Generation for German Legal Verdicts
Background
In this work, we try to automatically generate a legal norm chain for each verdict documentation. A legal norm consists of law code abbreviation, article number, paragraph number, etc. Those legal norms might have different granularity. A norm chain for a document includes the most relevant legal norms for the case and it represents a link between legal norms that explicitly or implicitly reference each other.
Traditionally, a legal norm chain for a verdict document is generated manually by the experts based on their domain knowledge. Therefore, in our work, we try to automate this manual process with a rule-based algorithm as well as machine learning or deep learning models.
Goal
- Research about the state-of-the-art NLP techniques for keyphrase extraction
- Design and implement a rule-based approach as well as ML or DL approaches to extract and assign the legal norm chain for the verdict documentation.
Research Questions
- How are the norm chains created by judges/legal authors?
- How to technically generate the norm chains for each verdict document?
- Can norm chains be generated just by the context in the respective verdict document? Do we need additional input information?
| Attribute | Value |
|---|---|
| Title (de) | Automatische Generierung von Normketten für deutschsprachige Urteile |
| Title (en) | Automatic Norm Chain Generation for German Legal Verdicts |
| Project | |
| Type | Master's Thesis |
| Status | completed |
| Student | Jieyi Zhang |
| Advisor | Alexandra Klymenko , Ingo Glaser |
| Supervisor | Prof. Dr. Florian Matthes |
| Start Date | 15.01.2020 |
| Sebis Contributor Agreement signed on | 09.12.2019 |
| Checklist filled | Yes |
| Submission date | 15.07.2020 |