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Chinese text sentiment analysis based on CNN-BiGRU network with attention mechanism
WANG Liya, LIU Changhui, CAI Dunbo, LU Tao
Journal of Computer Applications 2019, 39 (
10
): 2841-2846. DOI:
10.11772/j.issn.1001-9081.2019030579
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1833
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In the traditional Convolutional Neural Network (CNN), the information cannot be transmitted to each other between the neurons of the same layer, the feature information at the same layer cannot be fully utilized, making the lack of the representation of the characteristics of the sentence system. As the result, the feature learning ability of model is limited and the text classification effect is influenced. Aiming at the problem, a model based on joint network CNN-BiGRU and attention mechanism was proposed. In the model, the CNN-BiGRU joint network was used for feature learning. Firstly, deep-level phrase features were extracted by CNN. Then, the Bidirectional Gated Recurrent Unit (BiGRU) was used for the serialized information learning to obtain the characteristics of the sentence system and strengthen the association of CNN pooling layer features. Finally, the effective feature filtering was completed by adding attention mechanism to the hidden state weighted calculation. Comparative experiments show that the method achieves 91.93% F1 value and effectively improves the accuracy of text classification with small time cost and good application ability.
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Vertical union algorithm of interval concept lattices
ZHANG Ru, ZHANG Chunying, WANG Liya, LIU Baoxiang
Journal of Computer Applications 2015, 35 (
11
): 3213-3217. DOI:
10.11772/j.issn.1001-9081.2015.11.3213
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392
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To solve the practical problem that some rules may be lost when the association rules are extracted directly after the construction of interval concept lattice for the different formal context, the different interval concept lattices must be merged firstly. To improve the efficiency of lattice generating and consolidation, the incremental generation algorithm of interval concept lattice should be improved firstly, and then the concepts were stored in the form of structures which were divided into existence concepts, redundancy concepts and empty concepts. Secondly, the binary relation between extension and intension was analyzed and the sufficient condition of vertical merger, consistency of interval concept lattice, was defined. Thirdly the concepts which have consistent intension were divided into six kinds after merging and the corresponding decision theorem was given. In the end, based on the principle of breadth-first, a new vertical integration algorithm was designed through the type judgment and different processing methods of the concept lattice nodes in the original interval concept lattice. Finally, an application example verified the effectiveness and efficiency of the algorithm.
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