The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP
Abstract:Digital healthcare generates vast amounts of clinical text that can support AI-assisted applications, yet German biomedical language models remain limited by older architectures or restricted training data. We present ChristBERT (Clinical- and Healthcare-Related Issues and Subjects Tuned BERT), a family of domain-specific German RoBERTa-based language models trained on a 13.5GB corpus of scientific publications, clinical texts, health-related web content, and translated clinical resources. To investigate the impact of domain adaptation strategies in German clinical NLP, we compare continued pre-training, training from scratch, and domain-specific vocabulary adaptation. The resulting models are evaluated on three medical named entity recognition tasks and two text classification tasks. ChristBERT consistently outperforms existing general-purpose and medical German language models on four of five benchmarks and establishes a new state of the art for German clinical language modeling. Our results show that the optimal adaptation strategy is task-dependent: in our evaluation, training from scratch is particularly effective for highly specialized clinical texts, whereas continued pre-training performs well on more commonly written medical texts. All models are publicly released to support future research and applications in German medical NLP.
| Comments: | Under revision at BMC Medical Informatics and Decision Making |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.03250 [cs.CL] |
| (or arXiv:2606.03250v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.03250
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
Jun 3
-
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
Jun 3
-
IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation
Jun 3
-
Greener Than Humans? Environmental Attitudes in Large Language Models
Jun 3
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.