# AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning

arXiv: https://arxiv.org/abs/2211.16944 · HTML: https://mlsanity.com/paper/2211.16944/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [BioMegatron: Larger Biomedical Domain Language Model](https://mlsanity.com/paper/2010.06060/) (2020)
- [Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning](https://mlsanity.com/paper/1801.09851/) (2018)
- [HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition](https://mlsanity.com/paper/2008.07347/) (2020)
- [tmVar 3.0: an improved variant concept recognition and normalization tool](https://mlsanity.com/paper/2204.03637/) (2022)
- [Regularization for Long Named Entity Recognition](https://mlsanity.com/paper/2104.07249/) (2021)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)
- [Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework](https://mlsanity.com/paper/2205.03853/) (2022)

## Built on by (most cited first, 13 of 13)

- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition](https://mlsanity.com/paper/2309.12278/) (2023)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://mlsanity.com/paper/2504.00676/) (2025)
- [VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition](https://mlsanity.com/paper/2404.17835/) (2024)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [A Unified Biomedical Named Entity Recognition Framework with Large Language Models](https://mlsanity.com/paper/2510.08902/) (2025)
- [Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation](https://mlsanity.com/paper/2601.22124/) (2026)
- [Enhancing Biomedical Named Entity Recognition via Multiple Programming Languages Instruction Tuning and Ensemble Method](https://mlsanity.com/paper/2610.02949/) (2026)
- [EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical and Life Science Text](https://mlsanity.com/paper/2304.07805/) (2023)
- [Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset](https://mlsanity.com/paper/2508.19467/) (2025)
- [BELXTR: Biomedical Entity Linking via Contextualized Token Retrieval](https://mlsanity.com/paper/2609.25859/) (2026)

## Abstract (arXiv)

Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g., gene or disease). We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g., the entire PubMed data).