Improving chemical-induced disease relation extraction with learned features based on convolutional neural network

There have been an increasing number of various machine learning-based models successfully proposed and applied for automatic chemical-induced disease (CID) relation extraction. They, however, usually require carefully handcrafted rich feature sets, which rely on expert knowledge, thus require expensive human labor but normally still cannot generalize data well enough. In this paper, we propose a CID relation extraction model that learns features automatically through a Convolutional Neural Network (CNN) instead of traditional handcrafted features. We exploit the shortest dependency path between a disease and a chemical for identifying their CID relation. Dependency relations, with and without their direction information, are further investigated. Experimental results on benchmark datasets (namely the BioCreative V dataset) are very potential, demonstrating the effectiveness of our proposed model for CID relation extraction.