@inproceedings{gong-etal-2025-augmented,
    title = "Augmented Close Reading for Classical {L}atin using {BERT} for Intertextual Exploration",
    author = "Gong, Ashley  and
      Gero, Katy  and
      Schiefsky, Mark",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.nlp4dh-1.35/",
    doi = "10.18653/v1/2025.nlp4dh-1.35",
    pages = "403--417",
    ISBN = "979-8-89176-234-3",
    abstract = "Intertextuality, the connection between texts, is a critical literary concept for analyzing classical Latin works. Given the emergence of AI in digital humanities, this paper presents Intertext.AI, a novel interface that leverages Latin BERT (Bamman and Burns 2020), a BERT model trained on classical Latin texts, and contextually rich visualizations to help classicists find potential intertextual connections. Intertext.AI identified over 80{\%} of attested allusions from excerpts of Lucan's \textit{Pharsalia}, demonstrating the system's technical efficacy. Our findings from a user study with 19 participants also suggest that Intertext.AI fosters intertextual discovery and interpretation more easily than other tools. While participants did not identify significantly different types or quantities of connections when using Intertext.AI or other tools, they overall found finding and justifying potential intertextuality easier with Intertext.AI, reported higher confidence in their observations from Intertext.AI, and preferred having access to it during the search process."
}