MemoryRuleUncertainty

MemoryRuleUncertainty Text Semantic Parsing and Learning Process Digital Twin Modeling for Classical Literature Learning in Chinese Language Education. Overview MemoryRuleUncertainty is a framework designed to improve semantic parsing and learning process modeling for classical Chinese literature education. It combines memory stabilization, rule-guided reasoning, and uncertainty propagation to help analyze complex classical texts and model learners’ cognitive development. Key Features - Semantic parsing for classical Chinese literature- Learning process digital twin modeling- Memory Stabilization Adapter for temporal consistency- Rule Grounded State Binder for linguistic and pedagogical rule alignment- Uncertainty Propagation Resolver for ambiguity-aware interpretation- Temporal Dependency Modeling- Rule Guided Inference Architecture The framework consists of three core modules: 1. Memory Stabilization Adapter Maintains stable semantic representations across textual sequences. 2. Rule Grounded State Binder Aligns hidden representations with predefined linguistic and educational rules. 3. Uncertainty Propagation Resolver Handles ambiguity and uncertainty during semantic interpretation and learner-state modeling. Experimental Results The proposed method achieves improved performance across multiple datasets related to classical literature semantic parsing, Chinese language learning digital twins, text annotation, and semantic learning process tracking. Applications - Classical Chinese literature education- Semantic text analysis- Personalized learning systems- Digital twin modeling for education- Intelligent tutoring and assessment Future Work Future development will focus on lightweight modeling, automatic rule generation, and broader generalization across diverse classical literature datasets. License This project is released for academic and research purposes.