BiTCNN: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classification

Abstract

Relation classification is an important task in natural language processing (NLP) fields. State-of-the-art methods are mainly based on deep neural networks. This paper proposes a bi-channel tree convolution based neural network model, BiTCNN, which combines syntactic tree features and other lexical level features together in a deeper manner for relation classification. First, each input sentence is parsed into a syntactic tree. Then, this tree is decomposed into two sub-tree sequences with top-down decomposition strategy and bottom-up decomposition strategy. Each sub-tree represents a suitable semantic fragment in the input sentence and is converted into a real-valued vector. Then these vectors are fed into a bi-channel convolutional neural network model and the convolution operations re performed on them. Finally, the outputs of the bi-channel convolution operations are combined together and fed into a series of linear transformation operations to get the final relation classification result. Our method integrates syntactic tree features and convolutional neural network architecture together and elaborates their advantages fully. The proposed method is evaluated on the SemEval 2010 data set. Extensive experiments show that our method achieves better relation classification results compared with other state-of-the-art methods.