Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network
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Published:2023-11-09
Issue:22
Volume:12
Page:4578
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Jian Pengpeng1, Guo Fucheng2, Pan Cong1, Wang Yanli3, Yang Yangrui1, Li Yang1
Affiliation:
1. School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China 2. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China 3. College of Marxism, Henan University of Economics and Law, Zhengzhou 450046, China
Abstract
This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved using machines; however, machines encounter challenges in natural language processing and computer vision. Significant progress has improved 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 poor theorem prediction and poor accuracy in problem solving. In this paper, a formal language graph is constructed using the extracted formal language set and applied to theorem prediction using a graph convolutional network. To better extract the relationship set of diagram elements, an improved diagram parser is proposed. The test results indicate that the improved method has good results when solving interpretable geometry problems.
Funder
National Natural Science Foundation of China Science Foundation of Henan Province Major Project on Research and Practice of Higher Education Teaching Reform in Henan Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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