Modelling Intertextuality with N-gram Embeddings
This provides a scalable quantitative method for literary scholars to analyze intertextual relationships, though it is incremental as it builds on existing embedding techniques.
The paper tackled the problem of quantifying intertextuality in literary texts by proposing a new model using n-gram embeddings and pairwise comparisons, achieving validation on four texts with known intertextuality and scalability on 267 texts, with network analysis revealing centrality and community structures.
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.