# BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition

arXiv: https://arxiv.org/abs/2604.02904 · HTML: https://mlsanity.com/paper/2604.02904/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [HiNER: A Large Hindi Named Entity Recognition Dataset](https://mlsanity.com/paper/2204.13743/) (2022)
- [The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews](https://mlsanity.com/paper/2004.03659/) (2020)
- [Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework](https://mlsanity.com/paper/2407.04629/) (2024)
- [A Benchmark Evaluation of Clinical Named Entity Recognition in French](https://mlsanity.com/paper/2403.19726/) (2024)

## Abstract (arXiv)

In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-related articles from online Urdu news portals, medical prescriptions, and hospital health blogs and websites. After preprocessing, three native annotators with familiarity in the medical domain participated in the annotation process using the Doccano text annotation tool and annotated 153K tokens. Following annotation, the proposed BioiUNER dataset was evaluated both intrinsically and extrinsically. An inter-annotator agreement score of 0.78 was achieved, thereby validating the dataset as gold-standard quality. To demonstrate the utility and benchmarking capability of the dataset, we evaluated several machine learning and deep learning models, including Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), Multilingual BERT (mBERT), and XLM-RoBERTa. The gold-standard BioUNER dataset serves as a reliable benchmark and a valuable addition to Urdu language processing resources.