Transformer-Based Multilabel NER Using Wikipedia Corpora in Multiple Languages

The high cost of manual data labeling and privacy concerns result in a considerable dearth of medical annotations in non-English texts. Recent work by Frank and Kramer [1] introduces an unsupervised approach for constructing an ontology-annotated corpora from Wikipedia (https://www.wikidata.org) for German medical NER. We evaluate the proposed approach across English, German, Spanish, and French for medication and diagnosis entity recognition. Our multilabel corpora yield notable improvements in German medication detection under sparse annotations compared to the baseline, with consistent performance across other languages.

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Publication Details

Journal
Studies in health technology and informatics
Published
2025-05-15
DOI
https://doi.org/10.3233/shti250488
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Transformer-Based Multilabel NER Using Wikipedia Corpora in Multiple Languages

Frank Krämer, Johann Frei
Studies in health technology and informatics
Natural Language Processing Techniques
article

Transformer-Based Multilabel NER Using Wikipedia Corpora in Multiple Languages

Frank Krämer, Johann Frei
article en

Abstract

The high cost of manual data labeling and privacy concerns result in a considerable dearth of medical annotations in non-English texts. Recent work by Frank and Kramer [1] introduces an unsupervised approach for constructing an ontology-annotated corpora from Wikipedia (https://www.wikidata.org) for German medical NER. We evaluate the proposed approach across English, German, Spanish, and French for medication and diagnosis entity recognition. Our multilabel corpora yield notable improvements in German medication detection under sparse annotations compared to the baseline, with consistent performance across other languages.

Studies in health technology and informaticsVol. 327
University of Augsburg (DE)
Quality Education
Openalex Percentile: Top 81%
Natural Language Processing Techniques
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