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Markov chain analysis reveals structural shifts in Dante's Commedia

Researchers have developed a novel method to analyze the structural organization of Dante's Divine Comedy using a vowel-consonant encoding and Markov chain modeling. This approach quantifies graphemic memory, revealing a subtle but consistent increase in local dependency structure from Inferno to Paradiso. The study identifies specific graphemic patterns that link to lexical environments and highlights how orthographic conventions and cantica-specific terms influence textual organization. AI

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IMPACT Applies probabilistic modeling to text analysis, offering a new lens for literary studies.

RANK_REASON Academic paper analyzing text structure with probabilistic models.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Angelo Maria Sabatini ·

    From graphemic dependence to lexical structure: a Markovian perspective on Dante's Commedia

    arXiv:2604.22626v1 Announce Type: new Abstract: This study investigates the structural organisation of Dante's Divina Commedia through a symbolic representation based on vowel-consonant (V/C) encoding. Modelling the resulting sequence as a four-state Markov chain yields a parsimo…

  2. arXiv cs.CL TIER_1 · Angelo Maria Sabatini ·

    From graphemic dependence to lexical structure: a Markovian perspective on Dante's Commedia

    This study investigates the structural organisation of Dante's Divina Commedia through a symbolic representation based on vowel-consonant (V/C) encoding. Modelling the resulting sequence as a four-state Markov chain yields a parsimonious index of graphemic memory, capturing the b…