# CollaboNet: collaboration of deep neural networks for biomedical named entity recognition

arXiv: https://arxiv.org/abs/1809.07950 · HTML: https://mlsanity.com/paper/1809.07950/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 10 of 10)

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- [How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?](https://mlsanity.com/paper/2101.00160/) (2021)
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- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese](https://mlsanity.com/paper/2304.08999/) (2023)
- [MedicalBERT: enhancing biomedical natural language processing using pretrained BERT-based model](https://mlsanity.com/paper/2507.08013/) (2025)

## Abstract (arXiv)

Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.