Measuring and Mapping Intergeneric Allusion in Latin Poetry using Tesserae

Most intertextuality in classical poetry is unmarked, that is, it lacks objective signposts to make readers aware of the presence of references to existing texts. Intergeneric relationships can pose a particular problem as scholarship has long privileged intertextual relationships between works of the same genre. This paper treats the influence of Latin love elegy on Lucan’s epic poem, Bellum Civile, by looking at two features of unmarked intertextuality: frequency and distribution. I use the Tesserae project to generate a dataset of potential intertexts between Lucan’s epic and the elegies of Tibullus, Propertius, and Ovid, which are then aggregrated and mapped in Lucan’s text. This study draws two conclusions: 1. measurement of intertextual frequency shows that the elegists contribute fewer intertexts than, for example, another epic poem (Virgil’s Aeneid), though far more than the scholarly record on elegiac influence in Lucan would suggest; and 2. mapping the distribution of intertexts confirms previous scholarship on the influence of elegy on the Bellum Civile by showing concentrations of matches, for example, in Pompey and Cornelia’s meeting before Pharsalus (5.722-815) or during the affair between Caesar and Cleopatra (10.53-106). By looking at both frequency and proportion, we can demonstrate systematically the generic enrichment of Lucan’s Bellum Civile with respect to Latin love elegy

Affordances and limitations of algorithmic criticism

Humanities scholars currently have access to unprecedented quantities of machine-readable texts, and, at the same time, the tools and the methods with which we can analyse and visualise these texts are becoming more and more sophisticated. As has been shown in numerous studies, many of the new technical possibilities that emerge from fields such as text mining and natural language processing can have useful applications within literary research. Computational methods can help literary scholars to discover interesting trends and correlations within massive text collections, and they can enable a thoroughly systematic examination of the stylistic properties of literary works. While such computer-assisted forms of reading have proven invaluable for research in the field of literary history, relatively few studies have applied these technologies to expand or to transform the ways in which we can interpret literary texts. Based on a comparative analysis of digital scholarship and traditional scholarship, this thesis critically examines the possibilities and the limitations of a computer-based literary criticism. It argues that quantitative analyses of data about literary techniques can often reveal surprising qualities of works of literature, which can, in turn, lead to new interpretative readings

Modelling the Interpretation of Literary Allusion with Machine Learning Techniques

Most literary allusion, the deliberate evocation by one text of a passage in another, is based upon text reuse. Yet most instances of textual similarity are not meaningful literary allusions. The goal of the Tesserae projec