Intertext.AI: Augmented Close Reading for Classical Latin using AI for Intertextual Exploration. Gong, A. Ph.D. Thesis, Harvard University Engineering and Applied Sciences, Cambridge (MA), June, 2025. ![Intertext.AI: Augmented Close Reading for Classical Latin using AI for Intertextual Exploration [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Technological tools for computational language analysis, including applications for ancient languages like classical Latin, continue to improve as artificial intelligence (AI) models become more advanced. However, the subjective process of literary analysis is still primarily manual, relying on commentaries and secondary scholarship. Identifying and interpreting meaningful textual connections, or intertextuality, is labor-intensive and can often present a difficult learning curve for less experienced classicists. Although existing digital platforms offer complex searches by various linguistic qualities such as syntax and phrase similarity, they often do not enable comparisons of the results in their broader contexts, which can provide deeper insights that short excerpts do not reveal. Thus, this thesis proposes the use of AI and visualizations that prioritize contextualization to facilitate the discovery of intertextual correspondences using complex quantitative representations of texts. We introduce a novel web interface, Intertext.AI, that integrates Latin BERT (Bamman and Burns 2020), a machine learning language model trained on classical Latin texts, into contextually rich text visualizations to assist classicists in searching for potential intertextual connections. To evaluate the system, we tested its ability to find allusions attested in classical scholarship, investigated a case study on how a reader may use the interface to enhance their close reading, and conducted a user study with 19 participants who explored potential correspondence between two pairs of Latin poems. Intertext.AI identified over 80% of attested connections from excerpts of Lucan’s Pharsalia, demonstrating the system’s technical efficacy at detecting allusions. Further, while participants did not identify significantly different types or quantities of connections when using Intertext.AI or other tools, they overall found it easier to find and justify potential intertextuality with Intertext.AI, reported higher confidence in their observations identified through the interface, and preferred having access to it during the search process. These findings thus suggest that Intertext.AI facilitates meaningful intertextual discovery and interpretation by fostering literary comparison.
@phdthesis{gong_intertextai_2025,
address = {Cambridge (MA)},
type = {Bachelors {Thesis}},
title = {Intertext.{AI}: {Augmented} {Close} {Reading} for {Classical} {Latin} using {AI} for {Intertextual} {Exploration}},
shorttitle = {Intertext.{AI}},
url = {https://dash.harvard.edu/handle/1/42719198},
abstract = {Technological tools for computational language analysis, including applications for ancient languages like classical Latin, continue to improve as artificial intelligence (AI) models become more advanced. However, the subjective process of literary analysis is still primarily manual, relying on commentaries and secondary scholarship. Identifying and interpreting meaningful textual connections, or intertextuality, is labor-intensive and can often present a difficult learning curve for less experienced classicists. Although existing digital platforms offer complex searches by various linguistic qualities such as syntax and phrase similarity, they often do not enable comparisons of the results in their broader contexts, which can provide deeper insights that short excerpts do not reveal. Thus, this thesis proposes the use of AI and visualizations that prioritize contextualization to facilitate the discovery of intertextual correspondences using complex quantitative representations of texts. We introduce a novel web interface, Intertext.AI, that integrates Latin BERT (Bamman and Burns 2020), a machine learning language model trained on classical Latin texts, into contextually rich text visualizations to assist classicists in searching for potential intertextual connections. To evaluate the system, we tested its ability to find allusions attested in classical scholarship, investigated a case study on how a reader may use the interface to enhance their close reading, and conducted a user study with 19 participants who explored potential correspondence between two pairs of Latin poems. Intertext.AI identified over 80\% of attested connections from excerpts of Lucan’s Pharsalia, demonstrating the system’s technical efficacy at detecting allusions. Further, while participants did not identify significantly different types or quantities of connections when using Intertext.AI or other tools, they overall found it easier to find and justify potential intertextuality with Intertext.AI, reported higher confidence in their observations identified through the interface, and preferred having access to it during the search process. These findings thus suggest that Intertext.AI facilitates meaningful intertextual discovery and interpretation by fostering literary comparison.},
language = {en},
urldate = {2025-12-30},
school = {Harvard University Engineering and Applied Sciences},
author = {Gong, Ashley},
month = jun,
year = {2025},
}
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Although existing digital platforms offer complex searches by various linguistic qualities such as syntax and phrase similarity, they often do not enable comparisons of the results in their broader contexts, which can provide deeper insights that short excerpts do not reveal. Thus, this thesis proposes the use of AI and visualizations that prioritize contextualization to facilitate the discovery of intertextual correspondences using complex quantitative representations of texts. We introduce a novel web interface, Intertext.AI, that integrates Latin BERT (Bamman and Burns 2020), a machine learning language model trained on classical Latin texts, into contextually rich text visualizations to assist classicists in searching for potential intertextual connections. To evaluate the system, we tested its ability to find allusions attested in classical scholarship, investigated a case study on how a reader may use the interface to enhance their close reading, and conducted a user study with 19 participants who explored potential correspondence between two pairs of Latin poems. Intertext.AI identified over 80% of attested connections from excerpts of Lucan’s Pharsalia, demonstrating the system’s technical efficacy at detecting allusions. Further, while participants did not identify significantly different types or quantities of connections when using Intertext.AI or other tools, they overall found it easier to find and justify potential intertextuality with Intertext.AI, reported higher confidence in their observations identified through the interface, and preferred having access to it during the search process. 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We introduce a novel web interface, Intertext.AI, that integrates Latin BERT (Bamman and Burns 2020), a machine learning language model trained on classical Latin texts, into contextually rich text visualizations to assist classicists in searching for potential intertextual connections. To evaluate the system, we tested its ability to find allusions attested in classical scholarship, investigated a case study on how a reader may use the interface to enhance their close reading, and conducted a user study with 19 participants who explored potential correspondence between two pairs of Latin poems. Intertext.AI identified over 80\\% of attested connections from excerpts of Lucan’s Pharsalia, demonstrating the system’s technical efficacy at detecting allusions. 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