Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches
This addresses the need for more automated and less domain-dependent graph representations in NLP, though it is incremental as it builds on existing graph-based models.
The paper tackled the problem of graph-based document classification by proposing a method to learn data-driven graph structures instead of relying on heuristics, achieving higher accuracy and F1 scores on three datasets.
In document classification, graph-based models effectively capture document structure, overcoming sequence length limitations and enhancing contextual understanding. However, most existing graph document representations rely on heuristics, domain-specific rules, or expert knowledge. Unlike previous approaches, we propose a method to learn data-driven graph structures, eliminating the need for manual design and reducing domain dependence. Our approach constructs homogeneous weighted graphs with sentences as nodes, while edges are learned via a self-attention model that identifies dependencies between sentence pairs. A statistical filtering strategy aims to retain only strongly correlated sentences, improving graph quality while reducing the graph size. Experiments on three document classification datasets demonstrate that learned graphs consistently outperform heuristic-based graphs, achieving higher accuracy and $F_1$ score. Furthermore, our study demonstrates the effectiveness of the statistical filtering in improving classification robustness. These results highlight the potential of automatic graph generation over traditional heuristic approaches and open new directions for broader applications in NLP.