A Novel Graph-Sequence Learning Model for Inductive Text Classification
Published:Dec 24, 2025 05:00
•1 min read
•ArXiv NLP
Analysis
This paper introduces TextGSL, a novel graph-sequence learning model designed to improve inductive text classification. The model addresses limitations in existing GNN-based approaches by incorporating diverse structural information between word pairs (co-occurrence, syntax, semantics) and integrating sequence information using Transformer layers. By constructing a text-level graph with multiple edge types and employing an adaptive message-passing paradigm, TextGSL aims to learn more discriminative text representations. The claim is that this approach allows for better handling of new words and relations compared to previous methods. The paper mentions comprehensive comparisons with strong baselines, suggesting empirical validation of the model's effectiveness. The focus on inductive learning is significant, as it addresses the challenge of generalizing to unseen data.
Key Takeaways
- •Introduces TextGSL, a graph-sequence learning model for inductive text classification.
- •Addresses limitations of GNN-based approaches by incorporating diverse structural and sequential information.
- •Claims improved handling of new words and relations through adaptive message-passing and Transformer layers.
Reference
“we propose a Novel Graph-Sequence Learning Model for Inductive Text Classification (TextGSL) to address the previously mentioned issues.”