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Relation classification is an important semantic processing task in the fieldof natural language processing (NLP). In this paper, we present a novel model,Structure Regularized Bidirectional Recurrent Convolutional NeuralNetwork(SR-BRCNN), to classify the relation of two entities in a sentence, andthe new dataset of Chinese Sanwen for named entity recognition and relationclassification. Some state-of-the-art systems concentrate on modeling theshortest dependency path (SDP) between two entities leveraging convolutional orrecurrent neural networks. We further explore how to make full use of thedependency relations information in the SDP and how to improve the model by themethod of structure regularization. We propose a structure regularized model tolearn relation representations along the SDP extracted from the forest formedby the structure regularized dependency tree, which benefits reducing thecomplexity of the whole model and helps improve the $F_{1}$ score by 10.3.Experimental results show that our method outperforms the state-of-the-artapproaches on the Chinese Sanwen task and performs as well on the SemEval-2010Task 8 dataset\footnote{The Chinese Sanwen corpus this paper developed and usedwill be released in the further.

Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification