Researchtemporal annotationsclinical nlptransformerssynthetic data
Researchers Release TimeML-Compliant German Clinical Corpus
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Modersohn et al. (J Med Internet Res, 2026) present a TimeML-conformant annotation schema and apply it to two German clinical corpora, producing 3000PAJ-temp (non-distributable) and GraSCCo-temp (public synthetic). They report high NER interannotator agreement (F1=0.90) and trained BERT-based baseline taggers achieving NER F1 between 0.64–0.85 and temporal relation F1 between 0.60–0.64, enabling temporal extraction for German clinical NLP.
Scoring Rationale
High novelty and practical utility from first TimeML German corpus; scope limited to German clinical domain.
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