Relation Extraction via Position-Enhanced Convolutional Neural Network

Recently, deep neural network based methods have been widely used in relation extraction, which is an important task for knowledge base population, question answering and other natural language applications, to learn proper features from entities pairs and other sentence parts to extract relations from text. As a kind of important information, the value of position is always been underestimated, which causes a low weight of position information in various models and finally hurts the performance of relation extraction task. To alleviate this issue, we propose a position-enhanced embedding model based on convolutional neural network. In this model, we split the sentence representation into three parts based on the entity pairs in the sentence, and use three independent convolutional networks to learn features. Furthermore, we concatenate the output from different branches and employ a softmax layer to compute the probability for each relation. Experimental results on wildly used datasets achieve considerable improvements on relation extraction as compared with baselines, which shows that our proposed model can make full use of position information.