Sarah L. Jacobson’s research while affiliated with Buffalo State University and other places

The study of intertextuality, or how authors make artistic use of other texts in their works, has a long tradition, and has in recent years benefited from a variety of applications of digital methods. This article describes an approach for detecting the sorts of intertexts that literary scholars have found most meaningful, as embodied in the free Tesserae website http://tesserae.caset.buffalo.edu/. Tests of Tesserae Versions 1 and 2 showed that word-level n-gram matching could recall a majority of parallels identified by scholarly commentators in a benchmark set. But these versions lacked precision, so that the meaningful parallels could be found only among long lists of those that were not meaningful. The Version 3 search described here adds a second stage scoring system that sorts the found parallels by a formula accounting for word frequency and phrase density. Testing against a benchmark set of intertexts in Latin epic poetry shows that the scoring system overall succeeds in ranking parallels of greater significance more highly, allowing site users to find meaningful parallels more quickly. Users can also choose to adjust both recall and precision by focusing only on results above given score levels. As a theoretical matter, these tests establish that lemma identity, word frequency, and phrase density are important constituents of what make a phrase parallel a meaningful intertext.

Tesserae is a web-based tool for automatically detecting allusions in Latin poetry. Although still in the start-up phase, it already is capable of identifying significant numbers of known allusions, as well as similar numbers of allusions previously unnoticed by scholars. In this article, we use the tool to examine allusions to Vergil’s Aeneid in the first book of Lucan’s Civil War. Approximately 3,000 linguistic parallels returned by the program were compared with a list of known allusions drawn from commentaries. Each was examined individually and graded for its literary significance, in order to benchmark the program’s performance. All allusions from the program and commentaries were then pooled in order to examine broad patterns in Lucan’s allusive techniques which were largely unapproachable without digital methods. Although Lucan draws relatively constantly from Vergil’s generic language in order to maintain the epic idiom, this baseline is punctuated by clusters of pointed allusions, in which Lucan frequently subverts Vergil’s original meaning. These clusters not only attend the most significant characters and events but also play a role in structuring scene transitions. Work is under way to incorporate the ability to match on word meaning, phrase context, as well as metrical and phonological features into future versions of the program.

This paper describes a new digital approach to intertextual study involving the creation of a free online tool for the automatic detection of parallel phrases. A test comparison of Vergil’s Aeneid and Lucan’s Civil War shows that the tool can identify a substantial number of meaningful intertexts, both previously recorded and unrecorded. Analysis of these results demonstrates how automatic detection can provide more comprehensive and accessible perspectives on intertextuality as an aggregate phenomenon. Identification of the language features necessary to detect intertexts also provides a path toward improved automatic detection and more precise definitions of intertextuality.

In this study, we use computational methods to evaluate and quantify philological evidence that an eighth century CE Latin poem by Paul the Deacon was influenced by the works of the classical Roman poet Catullus. We employ a hybrid feature set composed of n-gram frequencies for linguistic structures of three different kinds—words, characters, and metrical quantities. This feature set is evaluated using a one-class support vector machine approach. While all three classes of features prove to have something to say about poetic style, the character-based features prove most reliable in validating and quantifying the subjective judgments of the practicing Latin philologist. Word-based features were most useful as a secondary refining tool, while metrical data were not yet able to improve classification. As these features are developed in ongoing work, they are simultaneously being incorporated into an existing online tool for allusion detection in Latin poetry.