L'eredità biblica nella letteratura cristiana antica in latino: un contributo alla mappatura delle relazioni intertestuali tramite sentence embeddings
DOI:
https://doi.org/10.60923/issn.2532-8816/22160Parole chiave:
Bibbie latine, patristica latina, intertestualità, sentence embeddings basati su BERT, IRCDL2025Abstract
Questo studio presenta una metodologia interdisciplinare per l’individuazione dei riferimenti biblici nella letteratura patristica latina, attraverso un intreccio innovativo di rigore filologico e tecniche di Natural Language Processing (NLP). Focalizzandosi su uno dei più significativi commentari cristiani antichi alla Bibbia, il De Genesi ad litteram di Agostino d’Ippona, e sul suo rapporto con i testi biblici in latino (in particolare la Vulgata di Gerolamo e le versioni precedenti), la ricerca introduce un sistema di classificazione dei riferimenti intertestuali basato su token, arricchito da annotazioni semantiche e supportato dalla piattaforma INCEpTION. La prima sezione dell'articolo illustra come questo sistema di classificazione numerica comprenda corrispondenze esatte, forme flesse, radici, sinonimi e altri tipi di parallelismi semantici (qui definiti “strutture”), catturando un ampio spettro di similarità testuale. Per migliorare il recupero automatico di queste connessioni intertestuali, alcuni modelli linguistici per il latino basati su BERT vengono sottoposti a fine-tuning, integrando tecniche di contrastive learning e hard negative mining. Nella seconda sezione, i risultati sperimentali mostrano che i modelli sottoposti a fine-tuning ottengono risultati nettamente migliori rispetto ai modelli di base a vari livelli di similarità testuale. Questo lavoro mette in evidenza l’utilità dei modelli computazionali nel superare la tradizionale dicotomia tra citazioni esplicite e allusioni implicite, accogliendo molteplici sfumature intermedie di similarità e offrendo un approccio scalabile allo studio dell’intertestualità negli scritti antichi.
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Copyright (c) 2026 Anna Mambelli, Laura Bigoni, Davide Dainese, Fabio Tutrone, Davide Caffagni, Federico Cocchi, Marco Zanella, Marcella Cornia, Rita Cucchiara
Questo lavoro è fornito con la licenza Creative Commons Attribuzione 4.0 Internazionale.
