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

• URL: http://arxiv.org/abs/2608.29890v1
• Date: Sun, 30 Aug 2026 16:40:18 GMT
• Title: En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations
• Abstract summary: This paper presents En-ViMedNER, the first English-Vietnamese parallel biomedical NER corpus annotated with UMLS semantic types.<n>The corpus contains PubMed 4,392 abstract pairs, 44,892 English-Vietnamese sentence pairs, and 202,949 aligned entity-mention pairs.<n>We characterize En-ViMedNER as a large-scale silver-standard corpus with a human-audited and consensus-corrected mini-test subset.
• Score: 8.997884931358152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ClinicalEncoder26AM: A Multlilingual Diagnosable ColBERT Model; Evidences from the MultiClinNER Shared Task [1.6904475483445454]
  ClinicalEncoder26AM is a multilingual Diagnosable ColBERT for clinical and biomedical texts.<n>We evaluate the model in the MultiClinNER shared task by finetuning it as a BIO tagger for patient symptoms, disorders, and procedure spans.
  arXiv  Detail & Related papers  (2026-05-27T14:20:45Z)
• ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark [7.798521826811972]
  Code-switching (CS) is when Vietnamese speech uses English words like drug names or procedures.<n>Current Automatic Speech Recognition systems struggle to recognize correctly English medical terms within Vietnamese sentences.<n>This work provides the first benchmark for Vietnamese medical code-switching.
  arXiv  Detail & Related papers  (2026-02-13T13:17:16Z)
• Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation [7.238888652441979]
  Medical English-Vietnamese machine translation (En-Vi MT) is essential for healthcare access and communication in Vietnam.<n>We evaluate prompting strategies for six multilingual LLMs (0.5B-9B parameters) on the MedEV dataset.
  arXiv  Detail & Related papers  (2025-09-19T06:06:36Z)
• MedQARo: A Large-Scale Benchmark for Medical Question Answering in Romanian [50.767415194856135]
  We introduce MedQARo, the first large-scale medical QA benchmark in Romanian.<n>We construct a high-quality and large-scale dataset comprising 102,646 QA pairs related to cancer patients.
  arXiv  Detail & Related papers  (2025-08-22T13:48:37Z)
• BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities [46.684778713683976]
  BiMediX2 is a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model.<n>It supports text-based and image-based medical interactions.<n>It enables multi-turn conversation in Arabic and English.
  arXiv  Detail & Related papers  (2024-12-10T18:59:35Z)
• Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
  We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:01:34Z)
• Medical Spoken Named Entity Recognition [18.348129901298652]
  We present VietMed-NER - the first spoken NER dataset in the medical domain.<n>To our knowledge, our Vietnamese real-world dataset is the largest spoken NER dataset in the world regarding the number of entity types.
  arXiv  Detail & Related papers  (2024-06-19T08:39:09Z)
• Towards Building Multilingual Language Model for Medicine [54.1382395897071]
  We construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages. We propose a multilingual medical multi-choice question-answering benchmark with rationale, termed as MMedBench. Our final model, MMed-Llama 3, with only 8B parameters, achieves superior performance compared to all other open-source models on both MMedBench and English benchmarks.
  arXiv  Detail & Related papers  (2024-02-21T17:47:20Z)
• BiMediX: Bilingual Medical Mixture of Experts LLM [90.3257333861513]
  We introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic.<n>Our model facilitates a wide range of medical interactions in English and Arabic, including multi-turn chats to inquire about additional details.<n>We propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations.
  arXiv  Detail & Related papers  (2024-02-20T18:59:26Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• Enriching Biomedical Knowledge for Low-resource Language Through Translation [1.6347851388527643]
  We make use of a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained as well as supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained-Decoder Transformer model trained on 20 million abstracts from the high-quality public corpus.
  arXiv  Detail & Related papers  (2022-10-11T16:35:10Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• A Multilingual Neural Machine Translation Model for Biomedical Data [84.17747489525794]
  We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags.
  arXiv  Detail & Related papers  (2020-08-06T21:26:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.