Abstract: Relation extraction is one of the important research topics in the field of information extraction.Its typical application scenarios include knowledge graphs,question answering systems,machine translation,etc.Recently,deep learning has been applied in a large amount of relation extraction researches,and deep neural networks based relationship extraction method performs much better than the traditional methods in many situations.However,most of the current deep neural network-based relation extraction methods Just rely on the corpus itself and lack the introduction of external knowledge.To address this issue,this paper proposed a neural network model,which combined encyclopedia knowledge and semantic features of sentences for relation extraction.The model introduced the description information of entities in encyclopedia as external knowledge,and dynamically extracted entity features through attention mechanism.Meanwhile,it employed bidirectional LSTM networks to extract the semantic features contained in the sentence.Finally,the model combined the entity features and the sentence semantic features for relation extraction.A series of experiments were carried out based on a manually labeled dataset.Experimental results demonstrate that the proposed model is superior to other existing relationship extraction methods.

Key words: Entity relation extraction, Chinese encyclopedia, Long short-term memory, Attention mechanism