Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on ...Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.展开更多
文摘Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.
