# BERN2: an advanced neural biomedical named entity recognition and normalization tool

arXiv: https://arxiv.org/abs/2201.02080 · HTML: https://mlsanity.com/paper/2201.02080/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [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)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (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)
- [Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature](https://mlsanity.com/paper/2009.02526/) (2020)

## Built on by (most cited first, 30 of 42)

- [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://mlsanity.com/paper/2310.07276/) (2023)
- [SciFact-Open: Towards open-domain scientific claim verification](https://mlsanity.com/paper/2210.13777/) (2022)
- [MolXPT: Wrapping Molecules with Text for Generative Pre-training](https://mlsanity.com/paper/2305.10688/) (2023)
- [BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning](https://mlsanity.com/paper/2402.17810/) (2024)
- [Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge](https://mlsanity.com/paper/2402.12352/) (2024)
- [LIQUID: A Framework for List Question Answering Dataset Generation](https://mlsanity.com/paper/2302.01691/) (2023)
- [A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding](https://mlsanity.com/paper/2406.05540/) (2024)
- [Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT](https://mlsanity.com/paper/2308.06294/) (2023)
- [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024)
- [PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes](https://mlsanity.com/paper/2406.13193/) (2024)
- [Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting](https://mlsanity.com/paper/2402.12801/) (2024)
- [Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking](https://mlsanity.com/paper/2307.01137/) (2023)
- [3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling](https://mlsanity.com/paper/2406.05797/) (2024)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [BELB: a Biomedical Entity Linking Benchmark](https://mlsanity.com/paper/2308.11537/) (2023)
- [Large Language Models Struggle in Token-Level Clinical Named Entity Recognition](https://mlsanity.com/paper/2407.00731/) (2024)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework](https://mlsanity.com/paper/2212.00223/) (2022)
- [A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models](https://mlsanity.com/paper/2409.13744/) (2024)
- [Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development](https://mlsanity.com/paper/2410.11550/) (2024)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment](https://mlsanity.com/paper/2509.07588/) (2025)
- [Enhancing Label Consistency on Document-level Named Entity Recognition](https://mlsanity.com/paper/2210.12949/) (2022)
- [Simple Data Augmentation Techniques for Chinese Disease Normalization](https://mlsanity.com/paper/2306.01931/) (2023)
- [Learning a Patent-Informed Biomedical Knowledge Graph Reveals Technological Potential of Drug Repositioning Candidates](https://mlsanity.com/paper/2309.03227/) (2023)
- [Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets](https://mlsanity.com/paper/2310.11715/) (2023)
- [Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs](https://mlsanity.com/paper/2312.13881/) (2023)
- [Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature](https://mlsanity.com/paper/2401.07447/) (2024)
- [Privacy Meets Explainability: Managing Confidential Data and Transparency Policies in LLM-Empowered Science](https://mlsanity.com/paper/2504.09961/) (2025)
- [TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching](https://mlsanity.com/paper/2505.08508/) (2025)

## Abstract (arXiv)

In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.