# Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models

arXiv: https://arxiv.org/abs/1802.01255 · HTML: https://mlsanity.com/paper/1802.01255/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Synthetic Dataset Generation of Driver Telematics](https://mlsanity.com/paper/2102.00252/) (2021)
- [Comparison of biomedical relationship extraction methods and models for knowledge graph creation](https://mlsanity.com/paper/2201.01647/) (2022)
- [BioBERT Based SNP-traits Associations Extraction from Biomedical Literature](https://mlsanity.com/paper/2308.02569/) (2023)
- [Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling](https://mlsanity.com/paper/2508.13833/) (2025)

## Abstract (arXiv)

Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge.