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.

本研究采用机器学习方法，结合支持向量机、卷积神经网络和递归神经网络的集成系统，自动检测 PubMed 摘要中的化学品 - 蛋白质关系，获得了高精度和高召回率表现，证明机器学习方法对于自动提取生物医学文献中的关系具有较高的有效性。