# Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework

arXiv: https://arxiv.org/abs/2009.09509 · HTML: https://mlsanity.com/paper/2009.09509/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets](https://mlsanity.com/paper/2306.11189/) (2023)
- [Deeper Clinical Document Understanding Using Relation Extraction](https://mlsanity.com/paper/2112.13259/) (2021)
- [Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application](https://mlsanity.com/paper/2306.05323/) (2023)
- [Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development](https://mlsanity.com/paper/2405.15766/) (2024)
- [ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs](https://mlsanity.com/paper/2503.06479/) (2025)
- [Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction](https://mlsanity.com/paper/2107.00596/) (2021)
- [Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives](https://mlsanity.com/paper/2504.03847/) (2025)

## Abstract (arXiv)

To minimize the accelerating amount of time invested in the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves overall the baselines (deep learning techniques) and single-task models for predicting the relationships, without compromising on the performance of all the tasks.