Fine-tuning a German legal language model with legal-entity tags modestly improves the content focus of automatically generated guiding principles for German court judgments, but the summaries still fall short of practice-ready quality.
A Dataset of German Legal Documents for Named Entity Recognition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Summarisation of German Judgments in conjunction with a Class-based Evaluation
Fine-tuning a German legal language model with legal-entity tags modestly improves the content focus of automatically generated guiding principles for German court judgments, but the summaries still fall short of practice-ready quality.