Abstract: Densely connected convolutional networks (DenseNet) is a new architecture of deep convolutional neural network.By using identity mapping for shortcut connections between different layers,it can ensure the maximum information transmission of neural network.In the distant supervised relation extraction task,precious models use shallow convolution neural networks to extract features of a sentence which can only represent partial semantic information.To enhance the representation power of network,a deep convolutional neural network model based on dense connectivity was designed to encode sentences.The proposed model consists of five layers of densely connected convolutional neural networks.It can capture more semantic information by combining different levels of lexical,syntactic,and semantic features.At the same time,it can alleviate the phenomenon of gradient disappearance of deep neural network,which makes the network more capable of characterizing natural language.The experimental results on NYT-Freebase datasets show that the mean accuracy of the proposed model achieves 82.5%,and the PR curve area achieves 0.43.Experimental results show that the proposed model can effectively utilize features and improve the accuracy of distant supervised relation extraction.

Key words: Convolutional neural network, Deep learning, Dense connectivity, Distant supervision, Relation extraction