# Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

arXiv: https://arxiv.org/abs/1801.09851 · HTML: https://mlsanity.com/paper/1801.09851/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 28 of 28)

- [Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences](https://mlsanity.com/paper/2201.11838/) (2022)
- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [A Comparative Study of Pretrained Language Models for Long Clinical Text](https://mlsanity.com/paper/2301.11847/) (2023)
- [Spark NLP: Natural Language Understanding at Scale](https://mlsanity.com/paper/2101.10848/) (2021)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning](https://mlsanity.com/paper/2211.16944/) (2022)
- [Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents](https://mlsanity.com/paper/2108.02899/) (2021)
- [A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction](https://mlsanity.com/paper/2201.10353/) (2022)
- [How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?](https://mlsanity.com/paper/2101.00160/) (2021)
- [Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition](https://mlsanity.com/paper/2309.12278/) (2023)
- [Clinical Named Entity Recognition using Contextualized Token Representations](https://mlsanity.com/paper/2106.12608/) (2021)
- [AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model](https://mlsanity.com/paper/2211.11363/) (2022)
- [Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization](https://mlsanity.com/paper/2305.13066/) (2023)
- [A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction](https://mlsanity.com/paper/2602.04320/) (2026)
- [PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning](https://mlsanity.com/paper/2102.13139/) (2021)
- [DMNER: Biomedical Entity Recognition by Detection and Matching](https://mlsanity.com/paper/2306.15736/) (2023)
- [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)
- [Few-shot Named Entity Recognition with Cloze Questions](https://mlsanity.com/paper/2111.12421/) (2021)
- [Semantics-Preserved Distortion for Personal Privacy Protection in Information Management](https://mlsanity.com/paper/2201.00965/) (2022)
- [Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"](https://mlsanity.com/paper/2201.09997/) (2022)
- [Hero-Gang Neural Model For Named Entity Recognition](https://mlsanity.com/paper/2205.07177/) (2022)
- [A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese](https://mlsanity.com/paper/2304.08999/) (2023)
- [Multi-Task Learning with Covariate-Overlap Regularization](https://mlsanity.com/paper/2505.24281/) (2025)
- [Effective Multi-Task Learning for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2507.18542/) (2025)
- [FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs](https://mlsanity.com/paper/2604.06297/) (2026)
- [DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition](https://mlsanity.com/paper/2604.15866/) (2026)
- [A Hybrid Method for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2605.04489/) (2026)

## Abstract (arXiv)

Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results: We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.