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Developing the German Medical Text Corpus (GeMTeX): Legal Compliance and Semantic Enrichment

Paper · 2026 · Justin Hofenbitzer, Christina Lohr, Andrea Riedel et al.

Summary

Clinical NLP outside English lacks data, mostly because privacy law and institutional rules make hospital texts hard to share. GeMTeX tackles this for German by building a national clinical reference corpus within the Medical Informatics Initiative (MII). The work has three stages. First, a legal foundation: only documents from patients who signed the nationwide MII Broad Consent are used, and one central ethics approval is recognised by all sites. Second, the consented documents from six university hospitals are de-identified. Third, the texts are annotated semantically, with spans grounded in Snomed CT concept identifiers so the annotations stay interoperable across languages. As of February 2026, the corpus counts over 15k documents, totalling around 20M tokens, along with 189k annotations. That already makes it far larger than the four previously accessible German clinical corpora, none of which exceeds 1M tokens or draws on more than two sites. The documents stay at their home hospitals and are combined only for approved projects. The paper also offers a reusable template for building GDPR-compliant clinical corpora.

Key points

  • The project has 18 partners. Six university hospitals (Munich, Leipzig, Erlangen, Berlin, Dresden, Essen) supply and annotate the documents, and the final corpus is expected to reach roughly 18k documents.
  • The legal basis is the MII Broad Consent under GDPR Art. 9 and Art. 89. Data are stored in pseudonymized, decentralized form, and a single IRB vote from TUM Klinikum is accepted by the other sites under the "one study, one vote" principle.
  • Annotation uses Snomed CT as its conceptual backbone. So far, inter-annotator agreement has been measured only in a pilot on a synthetic GraSCCo document.
  • Access requires a formal DUA through the Forschungsdatenportal Gesundheit, backed by a study protocol and an IRB vote. Approved users can also obtain linked structured data such as diagnosis codes, procedures and lab values.
  • Planned next steps:
    • a second domain layer covering cardiology, neurology, oncology and adverse drug events;
    • task suites for NER and Snomed CT normalization with hierarchy-aware scoring;
    • a finer-grained analysis of annotator disagreement.

Interesting ideas

  • I1. Tying text spans to language-neutral concept IDs, instead of to surface strings, lets annotations be reused across languages and joined with other resources.
  • I2. Hierarchy-aware scoring for concept normalization gives partial credit when a predicted concept is a close ancestor or sibling of the gold one. Plain exact-match scoring treats such near misses as total failures.
  • I3. The authors plan to split annotator disagreement into its sources: span boundaries, choice of concept, and stacked or overlapping labels. This turns a single agreement number into actionable error categories.
  • I4. Restricting access to a benchmark keeps it out of web-scraped training data, so it stays a fair test for LLM comparison over time.
  • I5. Collecting data from many sites dilutes the local jargon that skews corpora built at a single institution.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): Entities and events in the text world could carry WordNet/Wikidata identifiers, so knowledge-graph lookups and cross-text merging run on IDs rather than words.
  • I2 (learn): Evaluating frame and sense assignment against the WordNet/VerbNet hierarchy would separate slightly-too-general answers from truly wrong ones.
  • I3 (learn): When the witness parsers disagree on silver data, sorting the conflicts by type (attachment, label, tokenization) would show which repair rules or teacher tiers to target.
  • I4 (answer): Keeping some fables and school tests strictly private, never published or fed to LLM teachers, would give a clean held-out check on the readers.
  • I5 (tree): Drawing treebank and silver training text from deliberately varied sources and genres would stop the parser from overfitting one register's habits.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksBiochemistry, Genetics and Molecular Biology · 263 worksMolecular Biology · 260 works

🧭 Topics 1Research subjects and languages the work is about.Legal AI · 150 works

Cite

Cite as Justin Hofenbitzer, Christina Lohr, Andrea Riedel, Rebekka Kiser, Aliaksandra Shutsko, Abanoub Abdelmalak, Peter Klügl, Jutta Romberg, Sarah Riepenhausen, Miriam Schechner, Jakob Faller, Frank Meineke, Luise Modersohn, Markus Löffler, Juliane Fluck, Udo Hahn, Stefan Schulz and Martin Boeker. 2026. Developing the German Medical Text Corpus (GeMTeX): Legal Compliance and Semantic Enrichment. Proceedings of the Fifteenth Language Resources and Evaluation Conference.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4eqiegnqbu96

@inproceedings{hofenbitzer2026developing,
  title = {{Developing the German Medical Text Corpus (GeMTeX): Legal Compliance and Semantic Enrichment}},
  author = {Justin Hofenbitzer, Christina Lohr, Andrea Riedel, Rebekka Kiser, Aliaksandra Shutsko, Abanoub Abdelmalak, Peter Klügl, Jutta Romberg, Sarah Riepenhausen, Miriam Schechner, Jakob Faller, Frank Meineke, Luise Modersohn, Markus Löffler, Juliane Fluck, Udo Hahn, Stefan Schulz and Martin Boeker},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4eqiegnqbu96},
  url = {https://doi.org/10.63317/4eqiegnqbu96}
}

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