The study investigates whether cross‑lingual clinical annotation projection can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors evaluated this approach against supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores (up to 0.9201) and outperforms previous methods by 0.0564–0.1512, producing over 55,000 grounded mentions with accurate offsets.
By \'Alvaro Rey-Blanes, Francisco J. Moreno-Barea, Francisco J. Veredas
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
By Rakib Abdullah, Md. Maruful Islam Maruf
The study investigates whether cross‑lingual clinical annotation can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors compare this approach to supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores, surpassing previous state‑of‑the‑art by up to 0.15 and producing over 55,000 grounded mentions with accurate offsets.
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost.
"whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
By Genis Skura, Antoine Geissb\"uhler, Jean-Luc Falcone
The paper introduces a web‑data curation recipe for pretraining medical encoders, addressing the scarcity of large, diverse corpora in dense‑terminology domains like medicine. It proposes two complementary techniques: medical‑term density filtering to select documents rich in medical terminology, and signal‑amplifying rephrasing that uses an LLM to rewrite documents into denser variants with broader entity contexts. Applied to French medical NLP, the recipe produces the FineMed corpus and the DoctoBERT encoder family, achieving state‑of‑the‑art results on the DrBenchmark public benchmark and a proprietary clinical NER task.
By Bofeng Huang, Jacques Sun, Diane Bouchacourt, Nicolas Barascud, Fajwel Fogel
arXiv:2609.12544v1 Announce Type: new
Abstract: Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly...
By Linh Uyen Le, Christian Hoang, Huy Hoang Ha
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
By Yifan Zhang, Rahmatollah Beheshti
VietMed is a Vietnamese medical speech recognition dataset featuring 16 h of labeled medical speech, 1 000 h of unlabeled medical speech, and 1 200 h of unlabeled general‑domain speech. It claims to be the largest public medical ASR dataset worldwide in seven dimensions—total duration, speaker count, disease coverage, recording conditions, speaker roles, unique medical terms, and accents—and also the largest Vietnamese speech dataset by duration. The authors release pre‑trained models (w2v2‑Viet and XLSR‑53‑Viet) and fine‑tuned medical ASR models, showing that XLSR‑53‑Viet outperforms the state‑of‑the‑art XLSR‑53 by reducing WER from 51.8 % to 29.6 % on a medical test set.
whyItMatters":"The dataset and models provide the first large‑scale, publicly available Vietnamese medical ASR resource, enabling research and development of medical speech technologies in a language and domain that previously lacked sufficient data."
By Khai Le-Duc
arXiv:2609.37543v1 Announce Type: new
Abstract: Cross-lingual zero-shot transfer and multilingual fine-tuning are promising approaches for NLP tasks such as Named Entity Recognition (NER) in low-reso...
By Prosper Arineitwe Asiimwe, Francois Meyer, Jan Buys
The paper introduces MITE, a method that transforms biomedical named entity recognition (BioNER) into a structure‑to‑structure generation task by encoding instructions and outputs in multiple programming languages (Python, C++, Java). This approach provides structurally diverse supervision without extra biomedical knowledge, and during inference it aggregates predictions via entity‑level voting to reduce language‑specific variance. Experiments on six BioNER datasets show that MITE outperforms BERT‑based and LLM‑based baselines and generalizes well across datasets.
By Songtao Li, Yijia Zhang, Jianyuan Yuan, Shidi Zhang, Fengyu Zhang, Hongfei Lin
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv:2606. 01904v1 Announce Type: cross Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language.
By Christian Autenried, Cosimo Persia