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This research explores the development of an automatic parsing system for 3D analytic geometry problems using a Stacked Long Short-Term Memory (LSTM) neural network. The system aims to convert natural language statements of geometry problems into UCCA graph representations, enhancing the accuracy of parsing through a structured learning module. The study outlines the components and methodologies involved in creating this parser, emphasizing the challenges of natural language processing in the context of geo

Author

bian177

Language

EN