Tensor Graph Convolutional Networks for Text Classification (2001.05313v1)

Abstract: Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.

• The paper introduces the TensorGCN framework that integrates semantic, syntactic, and sequential information through intra- and inter-graph propagation.
• It leverages a novel text graph tensor to capture comprehensive textual context beyond traditional sequence-based models, enhancing classification performance.
• Experiments on benchmark datasets show that TensorGCN consistently outperforms both conventional and state-of-the-art methods in various text classification tasks.

Tensor Graph Convolutional Networks for Text Classification: An Expert Overview

The paper "Tensor Graph Convolutional Networks for Text Classification" presents a novel approach to the challenge of text classification by utilizing graph-based neural networks, specifically introducing the Tensor Graph Convolutional Networks (TensorGCN) framework. This approach leverages the complex structures of graph representations to capture a more comprehensive range of information from textual data compared to traditional sequential models.

The authors recognize that conventional text classification methods fall into two main categories: feature engineering with hand-crafted features and feature learning using machine learning models. While sequential-based models, such as CNNs and RNNs, focus on localsequence features, graph-based methods offer a more global perspective on data structures. TensorGCN is positioned to capitalize on these advantages, addressing the limitations of earlier graph neural networks (GNNs) for text classification.

Key Contributions

The paper introduces a text graph tensor that encapsulates semantic, syntactic, and sequential contextual information, offering a multi-faceted view of textual information which facilitates a more nuanced learning framework. The TensorGCN employs both intra-graph and inter-graph propagation learning methods:

1. Intra-Graph Propagation: This method aggregates information within each graph by leveraging neighborhood nodes, akin to traditional GCN approaches. It extracts comprehensive node representations by encoding graph structures and node features.
2. Inter-Graph Propagation: This novel approach within TensorGCN orchestrates information across heterogeneous graphs, harmonizing the distinct semantic, syntactic, and sequential contexts into a cohesive representation. This involves interactions between virtual graphs constructed over shared node sets to integrate diverse graph-specific features.

Experimental Validation

The authors conducted rigorous experiments on well-established benchmark datasets covering various text genres, including news categorization and medical literature classification. The empirical results indicate that TensorGCN consistently outperforms both traditional and state-of-the-art models like TextGCN and other graph-based methods across all datasets. The semantic, syntactic, and sequential graphs, evaluated individually and collectively, suggest that each graph type contributes uniquely to the end-to-end model performance. Notably, the graph tensor, which integrates all three types of graphs, yields the best performance in nearly all scenarios, emphasizing the benefits of combining multiple context-derived insights.

Discussion and Implications

This research underscores the potential of graph-based models in transcending the limitations inherent in sequence-based models for NLP tasks. By extending the capabilities of GCNs through tensor operations, the TensorGCN framework innovatively tackles the integration of heterogeneous information, paving the way for more adaptive and context-aware text classification systems.

Theoretical advancements like TensorGCN also signal promising avenues for future AI research, particularly in the areas of natural language understanding and multimodal information integration. The ability to synthesize diverse forms of contextual data into a synchronized learning process can have broad implications for AI applications that require a deep understanding of complex relationships within text data.

In conclusion, this paper contributes meaningfully to the landscape of text classification techniques, offering robust results that suggest significant theoretical and practical advancements. As NLP tasks continue to evolve, advancements like TensorGCN offer a compelling path forward for extracting richer semantic understanding from text data, with potential applications extending beyond traditional text classification tasks.