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.
