🤖 AI Summary
This study investigates whether Herman Melville’s reading influenced his writing at the semantic level. By leveraging BERTScore to compute sentence-level and 5-gram-level semantic similarity between Melville’s works and texts from his personal library, the research replaces conventional fixed thresholds with a semantic alignment metric to enable finer-grained analysis of literary influence. The approach integrates precision, recall, and F1 score for comprehensive evaluation, successfully reproducing established cases of influence documented in the scholarly literature while also uncovering several novel candidate passages suggestive of previously unrecognized connections. This work thus offers a verifiable and scalable computational framework for tracing literary sources and influences.
📝 Abstract
This study investigates the potential influence of Herman Melville reading on his own writings through computational semantic similarity analysis. Using documented records of books known to have been owned or read by Melville, we compare selected passages from his works with texts from his library. The methodology involves segmenting texts at both sentence level and non-overlapping 5-gram level, followed by similarity computation using BERTScore. Rather than applying fixed thresholds to determine reuse, we interpret precision, recall, and F1 scores as indicators of possible semantic alignment that may suggest literary influence. Experimental results demonstrate that the approach successfully captures expert-identified instances of similarity and highlights additional passages warranting further qualitative examination. The findings suggest that semantic similarity methods provide a useful computational framework for supporting source and influence studies in literary scholarship.