Questions Intent Classification Based on Dual Channel Convolutional Neural Network
YANG Zhiming1,2,3, WANG Laiqi3, WANG Yong2
1.Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China; 3.iDeepWise on Artificial Intelligence Robot Technology (Beijing) Co. Ltd, Beijing 100085, China
Abstract：Human-machine conversation technology has received extensive attention from the academic and industrial fields in recent years. The users question intention classification is an important key issues with direct effect on the quality of human-machine dialogue. In this paper, we propose an intent classification dual-channel Convolutional Neural Networks (ICDCNN) : we first extract semantic features by using Word2vec and Embedding layer to train the word vector ; then, two different channels are used for convolution, one for character level word vector, the other for word level word vector; thirdly, the character level word vectors (fine-grained) are combined with word level word vectors to mine deeper semantic information of natural language question; finally, with convolution kernels of different sizes, deeper abstract features inside the questions are learnt. Experimental results show that the algorithm achieves high accuracy on Chinese datasets, which has certain advantages compared to other methods.
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2019, Vol. 33 
