Christopher W. Forstall

The Tesserae Project: intertextual analysis of Latin poetry

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 Vergils Aeneid in the first book of Lucans Civil War. Approximately 3,000 lin- guistic 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 programs performance. All allusions from the program and commentaries were then pooled in order to examine broad patterns in Lucans allusive techniques which were largely unapproachable without digital methods. Although Lucan draws relatively con- stantly from Vergils generic language in order to maintain the epic idiom, this baseline is punctuated by clusters of pointed allusions, in which Lucan frequently subverts Vergils original meaning. These clusters not only attend the most sig- nificant 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.

Intertextuality in the digital age

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.

Modeling the scholars: Detecting intertextuality through enhanced word-level n-gram matching

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 . 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.

The sense of a connection: Automatic tracing of intertextuality by meaning

In literary study, intertextuality refers to the reuse of text, where new meaning or novel stylistic effects have been generated. Most typically in the digital humanities, algorithms for intertextual analysis search for approximate lexical correspondence that can be described as paraphrase. In this article, we look at a complimentary approach that more closely captures the behavior of the reader when faced with meaningful connections between texts in the absence of words that have the same form or stem, which constrains the match to semantics. The technique we employ for identifying such semantic intertextuality is the popular natural language processing strategy of semantic analysis. Unlike the typical scenario for semantic analysis, where a corpus of long form documents is available, we examine the far more limited textual fragments that embody intertextuality. We are primarily concerned with texts from antiquity, where small phrases or passages often form the locus of comparison. In this vein, we look at a specific case study of established parallels between book 1 of Lucan’s Civil War and all of Vergil’s Aeneid. Applying semantic analysis over these texts, we are able to recover parallels that lexical matching cannot, as well as discover new and interesting thematic matches between the two works.