corpus

In his 1966 essay “Rhétorique et enseignement,” Gérard Genette observes that literary studies did not always emphasize the reading of texts. Before the end of the nineteenth century, the study of literature revolved around the art of writing. Texts were not objects to interpret but models to imitate. The study of literature emphasized elocutio, or style and the arrangement of words. With the rise of literary history, academic reading approached texts as objects to be explained. Students learned to read in order to write essays (dissertations) where they analyzed texts according to prescribed methods. This new way of studying literature stressed dispositio, or the organization of ideas. Recent developments in information technology have further challenged paradigms for reading literature. Digital tools and resources allow for the study of large collections of texts using quantitative methods. Various computational methods of distant as well as close reading facilitate investigations into fundamental questions of the possibilities for literary creation. Technology has the potential for exploring inventio, or the finding of ideas that can be expressed through writing. One possibility is the Word Vector Topic Generator (https://github.com/mbwolff/WVTG), a Python script that makes use of vector space models of words. These models represent relationships between words from a defined corpus in spatial terms and can be used to calculate semantic similarities and differences. With a corpus and a given text it is possible to generate a new text according to how language was used within the corpus. Considered as an algorithmic topos in the Aristotelian sense, the WVTG instantiates an opposition to the thesis of an asserted text through analogy. Three inputs are required: the asserted text, the corpus from which a vector space model of words is derived, and a pair of words establishing an analogy for substitutions in the text. For instance, a corpus of 117 texts by Honoré de Balzac produces a vector space model of words that can generate a new text from Charles Baudelaire’s prose poem Enivrez-vous! by replacing each word in the poem with a word in the vector space model that best completes an analogy from the opposition bénir/maudire as expressed in Balzac’s writing. The code allows a user to easily experiment with different corpora and analogies to generate different texts. Unlike a traditional notion of invention positing that arguments to persuade an audience are discoverable within a shared and uncontested discursive space, the algorithmic invention of WVTG parameterizes both the discursive space and the relationships between words. Rhetorical invention such as this explores the potentiality of language as members of the Oulipo have done with techniques such as Jean Lescure’s S+7 method, Marcel Bénabou’s aphorism formulas and the ALAMO’s rimbaudelaire poems. The WVTG implements analytical tools from the digital humanities as a means for creating e-literature. With technology we can explore not only how something was written and why it was written, but also what was possible to write given a historical linguistic context.