Modelling the Interpretation of Literary Allusion with Machine Learning Techniques

A Computational Perspective on Allusion 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 project (http://tesserae.caset.buffalo.edu) is to automatically detect allusion in a corpus of literary texts, primarily Classical Latin poetry. We begin with a large set of textual parallels, and then attempt to model which of these instances of text reuse are meaningful literary allusions and which are not, according to a group of human readers. While initial attempts with a few basic textual features have proven surprisingly effective, here we employ a more complex feature set and machine learning techniques drawn from the field of computer vision in an attempt to improve the results. Novel applications of machine learning, beyond the well known but constrained textual classification tasks of attribution and categorization, have the potential to be transformative for complex analysis tasks in the Digital Humanities.