Natural language understanding and interaction engine oriented to human-computer interaction based on neural network

Yongbo Ma; Li Zhang; Xiaoyi Wang

doi:10.1117/12.2660383

3 February 2023 Natural language understanding and interaction engine oriented to human-computer interaction based on neural network

Yongbo Ma, Li Zhang, Xiaoyi Wang

Author Affiliations +

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251136 (2023) https://doi.org/10.1117/12.2660383
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

In recent years, due to the continuous advancement of computer software and hardware and the advancement of artificial intelligence, the advancement of human-machine communication systems has made considerable progress. The natural language understanding module is the basic element of the human-computer communication system, and the result of its semantic understanding will have a significant impact on the development of later components, and directly affect the process and the improvement of the success rate of human-computer interaction. In this paper, the neural network-oriented human-computer interaction-oriented natural language understanding and interaction engine is researched. On the basis of literature data, the relevant knowledge of human-computer interaction is understood, and then the neural network-oriented human-computer interaction natural language understanding is designed on the interactive engine, and the neural network reasoning structure model it quotes is tested. The test results show that the RMNN model used in this paper has achieved an accuracy of 70.24%, and the significance test p-value is 0.001.

Citation Download Citation

Yongbo Ma, Li Zhang, and Xiaoyi Wang "Natural language understanding and interaction engine oriented to human-computer interaction based on neural network", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251136 (3 February 2023); https://doi.org/10.1117/12.2660383

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