DeepAI AI Chat
Log In Sign Up

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

by   Yifan Peng, et al.

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.


page 1

page 2

page 3

page 4


BioRED: A Comprehensive Biomedical Relation Extraction Dataset

Automated relation extraction (RE) from biomedical literature is critica...

Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models

In Track-1 of the BioCreative VII Challenge participants are asked to id...

Does constituency analysis enhance domain-specific pre-trained BERT models for relation extraction?

Recently many studies have been conducted on the topic of relation extra...

Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction

The biomedical literature provides a rich source of knowledge such as pr...

A Machine Learning Framework for Automatic Prediction of Human Semen Motility

In this paper, human semen samples from the visem dataset collected by t...

A logic-based relational learning approach to relation extraction: The OntoILPER system

Relation Extraction (RE), the task of detecting and characterizing seman...

FoodChem: A food-chemical relation extraction model

In this paper, we present FoodChem, a new Relation Extraction (RE) model...