DOI: 10.5281/zenodo.17368805
title: NLG Grammar Graph (NLGGM + ALCS)

description: 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.