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

摘要：

　　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.

关键词： Relation classification Syntactic parsing tree Tree convolution Convolutional neural networks

作者: Feiliang Ren Yongcheng Li Rongsheng Zhao Di Zhou Zhihui Liu

作者单位: School of Computer Science and Engineering,Northeastern University,Shenyang 110819,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 158-170

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）