Explicit Grammar Semantic Feature Fusion for Robust Text Classification
arXiv:2602.20749v1 Announce Type: new Abstract: Natural Language Processing enables computers to understand human language by analysing and classifying text efficiently with deep-level grammatical and semantic features. Existing models capture features by learning from large corpora with transformer models, which are computationally intensive and unsuitable for resource-constrained environments. Therefore, our proposed study incorporates comprehensive grammatical rules alongside semantic information to build a robust, lightweight classification model without resorting to full parameterised transformer models or heavy deep learning architectures. The novelty of our approach lies in its explicit encoding of sentence-level grammatical structure, including syntactic composition, phrase patterns, and complexity indicators, into a compact grammar vector, which is then fused with frozen contextual embeddings. These heterogeneous elements unified a single representation that captures both the
arXiv:2602.20749v1 Announce Type: new Abstract: Natural Language Processing enables computers to understand human language by analysing and classifying text efficiently with deep-level grammatical and semantic features. Existing models capture features by learning from large corpora with transformer models, which are computationally intensive and unsuitable for resource-constrained environments. Therefore, our proposed study incorporates comprehensive grammatical rules alongside semantic information to build a robust, lightweight classification model without resorting to full parameterised transformer models or heavy deep learning architectures. The novelty of our approach lies in its explicit encoding of sentence-level grammatical structure, including syntactic composition, phrase patterns, and complexity indicators, into a compact grammar vector, which is then fused with frozen contextual embeddings. These heterogeneous elements unified a single representation that captures both the structural and semantic characteristics of the text. Deep learning models such as Deep Belief Networks (DBNs), Long Short-Term Memory (LSTMs), BiLSTMs, and transformer-based BERT and XLNET were used to train and evaluate the model, with the number of epochs varied. Based on experimental results, the unified feature representation model captures both the semantic and structural properties of text, outperforming baseline models by 2%-15%, enabling more effective learning across heterogeneous domains. Unlike prior syntax-aware transformer models that inject grammatical structure through additional attention layers, tree encoders, or full fine-tuning, the proposed framework treats grammar as an explicit inductive bias rather than a learnable module, resulting in a very lightweight model that delivers better performance on edge devices
Executive Summary
This study proposes a novel text classification model that leverages explicit grammar semantic feature fusion to improve robustness and performance in resource-constrained environments. By encoding sentence-level grammatical structure into a compact grammar vector and fusing it with frozen contextual embeddings, the model captures both structural and semantic characteristics of text. Experimental results demonstrate the model's superiority over baseline models, outperforming them by 2%-15%. The proposed framework's lightweight design and explicit grammar encoding make it suitable for edge devices. However, the study's scope is limited to text classification, and its applicability to other NLP tasks remains unclear.
Key Points
- ▸ Proposed a novel text classification model using explicit grammar semantic feature fusion
- ▸ Encodes sentence-level grammatical structure into a compact grammar vector
- ▸ Fuses grammar vector with frozen contextual embeddings for heterogeneous representation
- ▸ Achieved improved performance over baseline models in text classification tasks
Merits
Strength in Lightweight Design
The proposed model's lightweight design makes it suitable for resource-constrained environments, such as edge devices.
Improved Performance in Text Classification
The model outperformed baseline models by 2%-15% in text classification tasks, demonstrating its effectiveness.
Demerits
Limited Scope to Other NLP Tasks
The study's scope is limited to text classification, and its applicability to other NLP tasks, such as sentiment analysis or named entity recognition, remains unclear.
Dependence on Pre-trained Contextual Embeddings
The model's performance relies on the quality of pre-trained contextual embeddings, which may be a limitation in certain scenarios.
Expert Commentary
While the proposed model demonstrates promising results in text classification, its limitations and dependencies on pre-trained contextual embeddings need to be further explored. Additionally, the study's scope is narrow, and its applicability to other NLP tasks remains unclear. Nevertheless, the model's lightweight design and explicit grammar encoding make it a valuable contribution to the field of NLP. Future studies should aim to expand the model's scope and evaluate its performance in a broader range of NLP tasks.
Recommendations
- ✓ Future studies should aim to expand the model's scope to other NLP tasks and evaluate its performance in a broader range of applications.
- ✓ The model's dependency on pre-trained contextual embeddings should be addressed through the development of more robust and generalizable contextual embeddings.
