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En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations — arXiv:2608.29890

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29890
License (as archived): CC0-1.0
Archived: 2026-10-17T08:07:14.575Z
SHA-256 of the archived text: f5ea207c7a8ec8782007d8a86c912d25a28a94e53d62ed36fa400761fc805902
Stable URL of this page: https://metraipolis.com/documento/dc_6ffbcde26355

How to cite

En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations — arXiv:2608.29890 — arXiv (en), https://arxiv.org/abs/2608.29890 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6ffbcde26355 (Metraipolis archive dc_6ffbcde26355, sha256 f5ea207c7a8ec8782007d8a86c912d25a28a94e53d62ed36fa400761fc805902)

BibTeX

@misc{dc6ffbcde26355,
  title = {En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations — arXiv:2608.29890},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29890},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6ffbcde26355; sha256 f5ea207c7a8ec8782007d8a86c912d25a28a94e53d62ed36fa400761fc805902},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29890. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information extraction. While corpora with Unified Medical Language System (UMLS) annotations, such as MedMentions, have driven progress in English biomedical NER, no comparable resource exists for Vietnamese. This paper presents En-ViMedNER, the first English-Vietnamese parallel biomedical NER corpus annotated with UMLS semantic types, which are language-neutral codes providing a shared cross-lingual label space and ensuring direct comparability with existing UMLS-based resources. The corpus contains 4,392 PubMed abstract pairs, 44,892 English-Vietnamese sentence pairs, and 202,949 aligned entity-mention pairs across 21 semantic types adapted from the MedMentions ST21pv dataset. To balance quality and scalability, we have constructed the corpus through automatic translation, expert post-editing, LLM-assisted label projection, and human verification and adjudication. We characterize En-ViMedNER as a large-scale silver-standard corpus with a human-audited and consensus-corrected mini-test subset. We evaluate En-ViMedNER in two settings: (i) Vietnamese-input/Vietnamese-output biomedical NER and (ii) English-input/Vietnamese-output cross-lingual NER. For Vietnamese NER, we benchmark Vietnamese-supervised encoder models, English-supervised multilingual encoder models, and prompt-based LLMs. The best model achieves an F1 score of 52.70 on the test set and 53.78 on the mini-test set. For cross-lingual NER, we benchmark encoder-decoder models and prompt-based LLMs. The best model achieves an F1 score of 45.44 on the mini-test set. We publicly release our corpus, corpus construction pipeline, and baseline models to facilitate future Vietnamese biomedical NLP research.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2608.29890 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:47:52.751Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).