Jian, P.; Guo, F.; Pan, C.; Wang, Y.; Yang, Y.; Li, Y. Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network. Electronics2023, 12, 4578.
Jian, P.; Guo, F.; Pan, C.; Wang, Y.; Yang, Y.; Li, Y. Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network. Electronics 2023, 12, 4578.
Jian, P.; Guo, F.; Pan, C.; Wang, Y.; Yang, Y.; Li, Y. Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network. Electronics2023, 12, 4578.
Jian, P.; Guo, F.; Pan, C.; Wang, Y.; Yang, Y.; Li, Y. Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network. Electronics 2023, 12, 4578.
Abstract
This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved by machines, which still poses challenges in natural language processing and computer vision. Significant progress promotes existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to the poor effect of the theorem prediction and affect the accuracy in problem solving. In this paper, the formal language graph is constructed by the extracted formal language set and used to theorem prediction by graph convolutional network. So as to better extract the relationship set of diagram elements, an improved diagram parser was proposed. The test results indicate that the improved method has good results in solving the problem with interpretability geometry.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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