Research on natural language understanding model for power marketing strategies

Jianing Xu; Ying Jiang; Fei Lou; Qirui Chen; Yifan Zhang

doi:10.1117/12.3030627

5 June 2024 Research on natural language understanding model for power marketing strategies

Jianing Xu, Ying Jiang, Fei Lou, Qirui Chen, Yifan Zhang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316356 (2024) https://doi.org/10.1117/12.3030627
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

This article provides a detailed introduction to a method for constructing a few sample natural language understanding model based on encoding fusion. This method achieves knowledge unification and improves the applicability and accuracy of the model by encoding and fusing the rich general semantic knowledge in the pre trained model and the proprietary knowledge of marketing strategies in the power field in the knowledge graph. This model is capable of analyzing user input requests, transforming natural language input into predefined domains, intentions, and slots to achieve efficient understanding and analysis of issues in the field of power marketing. Finally, the effectiveness of this method was verified through a series of experiments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianing Xu, Ying Jiang, Fei Lou, Qirui Chen, and Yifan Zhang "Research on natural language understanding model for power marketing strategies", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316356 (5 June 2024); https://doi.org/10.1117/12.3030627

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