用自注意力学习文档图结构，自动建模句子关系
Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches
- 用自注意力模型自动学习句子间依赖关系构建图
- 在3个数据集上准确率和F1均高于传统启发式方法
- 适合需要自动建图的NLP任务，尤其文本分类
在文档分类中，图模型能有效捕捉文档结构，突破序列长度限制并增强上下文理解。然而，现有图表示多依赖启发式规则、领域特定规则或专家知识。不同于以往方法，本文提出一种数据驱动的图结构学习方法，无需人工设计，降低领域依赖。该方法以句子为节点构建同质加权图，通过自注意力模型学习句对间依赖关系，再结合统计过滤策略保留强相关句子，提升图质量并减小图规模。在三个文档分类数据集上的实验表明，所学图结构始终优于启发式图，实现更高准确率与F1分数。此外，统计过滤显著提升分类鲁棒性。结果表明，自动图生成优于传统启发式方法，为NLP中更广泛应用开辟新方向。
原文摘要 · Abstract (English)
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.
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