Abstract: To reduce the noise of labeled data in the distantly supervised relationship extraction, a distant supervision relationship extraction model integrating entity description and self-attention mechanism is proposed. Based on multi-instance learning, the comprehensive impacts of entity knowledge and position relation are considered, and the splicing vector of word, entity, entity description and relative position are adopted as the model input. A piecewise convolutional neural network is employed as the sentence encoder, which combines with the improved structured self-attention mechanism to capture the internal correlation of features. The difference vector between tail entity and head entity is constructed as the supervision information of attention mechanism to assign weight to each sentence. Experimental results on New York Times dataset show that the model performance indexes of the model reach the maximum values when compared to state-of-the-art models. 

Key words: relation extraction, entity, entity description, piecewise convolutional neural network, self-attention mechanism