Intelligent question and answer analysis model of power ICT based on BI-LSTM-CRF
| Autor: | Hu Feifei, Zeng Shibo, Hong Danke, Zhang Situo, Song Yongwei, Xu Cheng |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Modeling, Simulation, and Scientific Computing. |
| ISSN: | 1793-9615 1793-9623 |
| DOI: | 10.1142/s1793962323410106 |
| Popis: | With the deepening of the country’s digital transformation and the development of big data artificial intelligence software definition and other technologies, customers’ demand for IT services is constantly increasing. In the electric power industry, the front-end knowledge base for traditional customer service lacks self-learning ability, the accumulated customer service data in the middle cannot be analyzed and utilized effectively, the back-end quality inspection was not fully covered and lacked emotion recognition, etc. Apply artificial intelligence, information and communication technology and big data technology to power ICT customer service. It is proposed to construct a network model by combining conditional random airport with the long- and short-term memory neural network, and to realize the intelligent customer service model combined with human–computer interaction by constructing an intelligent knowledge base and the information system fault association analysis model, so as to improve the extraction performance of context information and establish a knowledge base question answering system in the field of power service. Through the comparative analysis of experimental data, the proposed model has achieved good results and can be effectively applied to the power industry to improve customer service satisfaction. |
| Databáze: | OpenAIRE |
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