Research on intelligent government question answering system with autonomous learning and memory function
Once a task-based question answering system is built, it is usually fixed and can answer very limited questions, making it difficult to meet user needs. A method for automatically updating the knowledge base in real-time was proposed. When a user asks a question that the question answering system ca...
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2024-01-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000163430 |
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| author | Fang Haiquan Deng Mingming |
| author_facet | Fang Haiquan Deng Mingming |
| author_sort | Fang Haiquan |
| collection | DOAJ |
| description | Once a task-based question answering system is built, it is usually fixed and can answer very limited questions, making it difficult to meet user needs. A method for automatically updating the knowledge base in real-time was proposed. When a user asks a question that the question answering system cannot answer, the system will automatically send the question to the manual customer service. After the manual customer service used professional knowledge to reply, the system can automatically obtain the user's question and the answer replied by the manual customer service in real time, and automatically update the question answering pair to the knowledge base in real time. If other users ask similar questions, the question answering system can quickly provide corresponding to answers. Taking the question answering system in the field of government affairs as an example, the text vectorization method ERNIE was applied to build a question answering system that automatically updates the knowledge base in real time. After computer experiments, it has been proven that the proposed method can achieve automatic real-time updates of the knowledge base, and the constructed question answering system has autonomous learning and memory functions, improving the intelligence level of the task-based question answering system. |
| format | Article |
| id | doaj-art-8db121c187cc48fb8b41bb2e85d64bf6 |
| institution | Kabale University |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-8db121c187cc48fb8b41bb2e85d64bf62025-08-20T03:27:52ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982024-01-01501212610.16157/j.issn.0258-7998.2340753000163430Research on intelligent government question answering system with autonomous learning and memory functionFang Haiquan0Deng Mingming1Zhejiang University of Technology， Hangzhou 310023， ChinaZhejiang University of Technology， Hangzhou 310023， ChinaOnce a task-based question answering system is built, it is usually fixed and can answer very limited questions, making it difficult to meet user needs. A method for automatically updating the knowledge base in real-time was proposed. When a user asks a question that the question answering system cannot answer, the system will automatically send the question to the manual customer service. After the manual customer service used professional knowledge to reply, the system can automatically obtain the user's question and the answer replied by the manual customer service in real time, and automatically update the question answering pair to the knowledge base in real time. If other users ask similar questions, the question answering system can quickly provide corresponding to answers. Taking the question answering system in the field of government affairs as an example, the text vectorization method ERNIE was applied to build a question answering system that automatically updates the knowledge base in real time. After computer experiments, it has been proven that the proposed method can achieve automatic real-time updates of the knowledge base, and the constructed question answering system has autonomous learning and memory functions, improving the intelligence level of the task-based question answering system.http://www.chinaaet.com/article/3000163430question answering systemautonomous learningmemory functionknowledge baseautomatic real-time updates |
| spellingShingle | Fang Haiquan Deng Mingming Research on intelligent government question answering system with autonomous learning and memory function Dianzi Jishu Yingyong question answering system autonomous learning memory function knowledge base automatic real-time updates |
| title | Research on intelligent government question answering system with autonomous learning and memory function |
| title_full | Research on intelligent government question answering system with autonomous learning and memory function |
| title_fullStr | Research on intelligent government question answering system with autonomous learning and memory function |
| title_full_unstemmed | Research on intelligent government question answering system with autonomous learning and memory function |
| title_short | Research on intelligent government question answering system with autonomous learning and memory function |
| title_sort | research on intelligent government question answering system with autonomous learning and memory function |
| topic | question answering system autonomous learning memory function knowledge base automatic real-time updates |
| url | http://www.chinaaet.com/article/3000163430 |
| work_keys_str_mv | AT fanghaiquan researchonintelligentgovernmentquestionansweringsystemwithautonomouslearningandmemoryfunction AT dengmingming researchonintelligentgovernmentquestionansweringsystemwithautonomouslearningandmemoryfunction |