Abstract: Most of relationship extraction methods cannot obtain long-distance dependent information from long sentences, and the performance of relationship extraction is degraded due to the data noise. This paper proposes a new relationship extraction model based on BiLSTM and ResCNN to solve these problems. The model uses BiLSTM to obtain the context information vector of words. The features of the middle or low layer in the convolutional neural network are transferred to the high layer through residual network, which effectively solves the problem of vanishing gradient. At the same time, embedding the squeeze-and-excitation block into the residual network can greatly reduce the data noise and strengthen the feature transfer. The piecewise max pooling method is used to capture the structural information of the entity pair. This paper designs verification experiments on NYT-Freebase dataset. Experimental results show that this model can fully learn features and significantly improve the effect of relationship extraction.

Key words: relation extraction, distant supervision, convolutional neural network, residual network, long short-term memory