用n-gram嵌入量化文本间引用关系，实现可扩展的文学关联分析。
Modelling Intertextuality with N-gram Embeddings
- 通过对比两文本n-gram嵌入的相似度，平均得整体互文性得分。
- 在4个已知互文强度的文本上验证有效，267个文本测试展现高效性。
- 可揭示文本网络中的核心节点与社区结构，适合文学分析与数字人文研究。
互文性是文学研究的核心概念，指文本间通过各类引用建立的复杂联系。本文提出一种新的定量互文性模型，通过计算两文本n-gram嵌入的成对相似性并取均值，实现可扩展的分析与网络洞察。在4个已知互文程度的文本上进行验证，并对267个多样化文本进行可扩展性测试，结果表明该方法高效且有效。网络分析进一步揭示了中心性与社群结构，证实其在捕捉和量化互文关系方面的成功。
原文摘要 · Abstract (English)
Intertextuality is a central tenet in literary studies. It refers to the intricate links between literary texts that are created by various types of references. This paper proposes a new quantitative model of intertextuality to enable scalable analysis and network-based insights: perform pairwise comparisons of the embeddings of n-grams from two texts and average their results as the overall intertextuality. Validation on four texts with known degrees of intertextuality, alongside a scalability test on 267 diverse texts, demonstrates the method's effectiveness and efficiency. Network analysis further reveals centrality and community structures, affirming the approach's success in capturing and quantifying intertextual relationships.
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