Abstract：To effectively obtain the conformance clauses in the transportation information standards and simplify the standard methods, combining the bidirectional long short-term memory （BLSTM）based text enhancement representation with the CNN based sentence classification, the classification method was proposed to classify the conformance clauses for solving the problems of lack of context meaning in convolution neural network and gradient disappearance and gradient dispersion in cyclic neural network in the existing text classification methods. The core idea was to add the vectors generated by the forward and backward processes of BLSTM, and the added vectors were spliced with the original vector as vector representation of the text. The text was classified as the input of CNN network. To verify the proposed model, the comparative test with traditional TF-IDF+SVM machine model, single CNN, BLSTM neural network model and classic hybrid model was set up. According to the test of the data set of standard terms of transportation information, the accuracy of the chain-mixed neural network model based on the improved BLSTM and CNN reaches 93.77%.
范维克, 张绍阳, 陈博远, 王珂. 交通信息标准条款BLSTM和CNN链式模型分类方法[J]. 江苏大学学报（自然科学版）, 2020, 41(2): 143-148.
FAN Weike, ZHANG Shaoyang, CHEN Boyuan, WANG Ke. Classification methods of traffic standard terms based on BLSTM and CNN chain model[J]. Journal of Jiangsu University(Natural Science Eidtion)
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2020, Vol. 41 
