Text Level Graph Neural Network for Text Classification (1910.02356v2)

Abstract: Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information. However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which do not support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.

• The paper introduces a text-level graph construction method that builds individual graphs per text, significantly reducing memory consumption compared to global graphs.
• The paper employs a global parameter sharing strategy combined with a message passing mechanism to integrate local context with global insights for efficient online testing.
• The paper demonstrates state-of-the-art results by achieving 97.8% accuracy on the R8 dataset with a fraction of the memory usage of previous models.

An Analysis of "Text Level Graph Neural Network for Text Classification"

The paper "Text Level Graph Neural Network for Text Classification" by Lianzhe Huang et al. addresses the application of Graph Neural Networks (GNNs) to text classification tasks. The researchers aim to overcome the limitations associated with the use of global graph structures, which typically pair high memory consumption with poor compatibility for online testing scenarios. Their proposed solution is a novel GNN model that operates on a text level, constructing individual graphs for each input instance and sharing global parameters across these graphs. This approach seeks to retain the benefits of global information in a more memory-efficient manner while enabling robust online testing capabilities.

Key Contributions

1. Text-Level Graph Construction: Unlike previous GNN-based models that construct a single corpus-wide graph, this model creates a graph for each individual text. It connects word nodes within a specific, narrow window, thereby capturing local features effectively and reducing the number of edges significantly, which lowers memory usage.
2. Parameter Sharing and Reduction of Dependencies: By sharing representations of node weights globally, the model ensures that textual representations benefit from global insights while still maintaining local context. This design removes the dependency of a single text graph on the entirety of the corpus, allowing for more efficient online testing.
3. Message Passing Mechanism: The model employs a non-spectral convolution technique through a message passing mechanism, where node representations in a graph are updated by aggregating information from their neighbors. This mechanism employs a reduction function targeting the maximum values across dimensions, enhancing the interpretative capacity of nodes within their contextual surroundings.
4. State-of-the-Art Results with Lesser Memory Usage: Experimental validation shows that the proposed approach achieves state-of-the-art accuracy in several benchmark datasets (R8, R52, and Ohsumed) with significantly reduced memory consumption compared to existing methods, such as Text-GCN.

Experimental Evaluation

The paper presents comprehensive experimental results supporting their claims. Notably, their method yields an accuracy improvement over the existing Text-GCN while consuming markedly less memory. For instance, in the R8 dataset, their model achieves 97.8% accuracy with memory consumption nearly one-tenth that of Text-GCN. This validates the authors' assertion that local graph construction paired with global parameter sharing is beneficial for text classification tasks.

Implications and Future Directions

From a practical standpoint, this model presents a viable solution for applications requiring real-time classification with constrained resource availability. Reduced memory usage potentially facilitates broader deployment across computational environments, including edge devices or cloud-based platforms where resources are at a premium.

The results suggest intriguing prospects for extending graph-based models in other text processing and classification tasks, potentially benefiting areas such as complex entity relation extraction or sentiment analysis involving context-adaptive semantics. Furthermore, the text-level graph approach may find applications in domains requiring per-instance graph generation, including real-time recommendation systems and personalized content curation.

Conclusion

The "Text Level Graph Neural Network for Text Classification" paper makes a significant contribution to the field of natural language processing by configuring GNNs in a text-level structure that enables both effective classification and resource efficiency. This work underscores the potential of GNNs beyond their traditional applications, paving the way for further research that explores nuanced graph structures tailored to the unique demands of textual data. Future research may concentrate on optimizing node and edge weight sharing techniques, exploring alternative message passing mechanisms, or extending the methodology to handle multilingual or cross-domain text corpora.