中圖分類號(hào)：TP391
文獻(xiàn)標(biāo)識(shí)碼：A
DOI： 10.19358/j.issn.2096-5133.2020.01.013
引用格式：時(shí)昭麗，范紅，陳佳偉，等。融合位置信息的卷積門(mén)控網(wǎng)絡(luò)實(shí)現(xiàn)與應(yīng)用［J］。信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（1）：70-73.

Implementation and application of convolution gating network model combined with position information

Abstract： Aspectbased sentiment analysis usually uses longshortterm network and the attention mechanism method，the two models are complicated in structure and long time in running time.The existing convolutional neural network has a simple structure,and is representative of a GCAE （Gated Convolutional Networks with Aspect Embedding） model.Since the position information of the words does not fully utilized，the keywords cannot be accurately and quickly focused.Therefore,this paper proposed a convolutional gating network method that combines position information.The SemEval dataset was used for experiments and compared with the experimental results using the GCAE model.The results show that the iteration time of this model is about 5.96 s，which is better than the LSTM model of 81 s. The accuracy of this model for multiple aspects of sentences and multiple emotional polarity is 55.00%, which is higher than 53.00% of the GCAE model.This paper has certain reference significance for improving the iterative time and accuracy of aspectbased sentiment analysis.

Key words : aspect-based sentiment analysis;position information;convolutional neural network; gating mechanism