A combination of RNN and CNN for attention-based relation classification

Abstract

Relation classification plays an important role in the field of natural language processing (NLP). Previous research on relation classification has verified the effectiveness of using convolutional neural network (CNN) and recurrent neural network (RNN). In this paper, we proposed a model that combine the RNN and CNN (RCNN), which will Give full play to their respective advantages: RNN can learn temporal and context features, especially long-term dependency between two entities, while CNN is capable of catching more potential features. We experiment our model on the SemEval-2010 Task 8 dataset 1 , and the result shows that our method is superior to most of the existing methods.