NLG Grammar Graph (NLGGM + ALCS)

NLGGM: Nerve Logic Grammar Graph Model Overview NLGGM (Nerve Logic Grammar Graph Model) is a novel framework that integrates nerve logic principles with graph-based representations to build grammar graphs and perform automatic language error repair.This framework combines neural network architectures, graph theory, and logical inference to detect, interpret, and repair grammatical inconsistencies in natural language with high accuracy. At its core, NLGGM leverages: Graph-based sentence representation Logical constraints for grammar structure Neural computation for adaptive learning Automated Linguistic Correction Strategy (ALCS) This enables real-time, explainable, and scalable language error detection and correction. ✨ Features Grammar Graph Construction Represents sentences as directed graphs where nodes correspond to words and edges to syntactic/semantic relations. Graph weights encode linguistic strength and compatibility. Logical Inference Integration Applies nerve logic constraints to enforce grammatical coherence. Uses rule-based logical evaluation combined with neural inference. Automated Error Detection & Repair Identifies topological irregularities in grammar graphs. Repairs errors by targeted graph transformations guided by logical predicates. Multimodal Encoder Architecture Incorporates multi-receptive field convolution, self-attention, and logical inference layers. Real-time adaptation to linguistic context. Probabilistic Path Selection Uses a probabilistic model to resolve language ambiguities. Selects the most likely correction path using maximum-likelihood inference. 📊 Datasets Dataset Description Key Use Neural Grammar Structure Dataset Sentences with annotated grammatical structures Grammar graph construction Language Error Correction Patterns Sentences with error annotations and corrections Training ALCS Syntax Graph Construction Data Graph-based syntactic sentence structures Syntax representation Automated Language Repair Samples Annotated error-repair pairs Automatic repair training These datasets enable training and evaluation of both grammar graph generation and error repair components. ⚙️ Installation # Clone the repository git clone https://zenodo.org/records/17368805 🚀 Usage Graph structure before and after repair Predicted error types and corrections Logical constraint satisfaction score 🧪 Applications Real-time grammar checking and repair Intelligent language learning systems Syntax graph construction for NLP pipelines Transparent, explainable linguistic modeling 🧩 Model Components Nerve Logic Grammar Graph Model (NLGGM) — grammar graph construction Multimodal Encoder Architecture — convolution, pooling, attention (see Fig. 2, p.8) Graphical Propagation Layer — dynamic edge and node feature refinement Logical Inference Engine — evaluates grammatical constraints (see Fig. 1, p.7) Automated Linguistic Correction Strategy (ALCS) — error detection and repair (see Fig. 3, p.9) 📈 Performance Dataset Accuracy Recall F1 Score AUC Neural Grammar Structure 89.34 88.78 88.12 88.45 Language Error Correction Patterns 91.12 90.67 90.01 90.34 Syntax Graph Construction 89.12 88.56 87.89 88.15 Automated Language Repair 91.02 90.45 89.78 90.03 NLGGM outperforms traditional baselines such as ResNet, ViT, and BLIP on multiple benchmarks (see Tables 1–2, p.12–13). 🧭 Future Work Optimize graph construction and transformation for real-time deployment Extend to low-resource languages Enhance contextual semantic understanding for better repair accuracy Integrate with multilingual language learning tools 📜 License This project is licensed under the MIT License. 🙏 Acknowledgments This work was developed at Beijing Jiaotong Vocational Technical College.Authors: Ying Zhang (corresponding author), Wei Fan.This research integrates insights from symbolic AI, deep learning, and graph theory to advance language error correction.