Augmented Criticism Lab
Preface: Knowledge and Information
Shall I compare thee, human, to a machine? Thou art more critical and more intemperate (Shakespeare, Sonnet 18).
But seriously: how do human readers compare to machines? I ask because I want to define how literary critics can use machines to augment and extend our readings. Figuring that out depends on an understanding of how our readings compare to the machine’s abilities. Sure, they’re faster: but faster at what, exactly?
This week I’m away to the Pacific Northwest Renaissance Conference to deliver a paper on rhetorical figures in early modern drama. (Wait! Don’t stop reading, it gets better.) I feel like a legit digital humanist for the first time in my life, because I’ve written my own computer program to analyze texts – a bash script in Unix that you can try for yourself on Github.
“Ask not what your country can do for you.” Instead, ask what the next line is from President Kennedy’s 1961 inaugural address. Most will remember the second part of that familiar sentence: “but what you can do for your country.” It’s memorable because it repeats three words and phrases from the first half, just in inverse order: “you,” “can do,” and “your country.”
The term for this kind of linguistic structure is a rhetorical figure, and the term for this kind of rhetorical figure is antimetabole: a symmetrical (ABC|CBA) arrangement of words and phrases.
Can you trust machines to make decisions on your behalf? You’re doing it already, when you trust the results of a search engine or follow directions on your phone or read news on social media that confirms your worldview. It’s so natural that you forget it’s artificial; someone programmed a machine to make it happen. If Arthur C. Clarke is right (“any sufficiently advanced technology is indistinguishable from magic”), we’re living in the age of magical thinking.
I’m giving a talk on the University of Calgary campus (in SS 1015) on Friday December 2, 2016 at 3:15pm.
Unnatural Language and Natural Thinking: Shakespeare and His Contemporaries
Critics of computational text-analysis tend to perceive its focus on language patterns as a flattening of qualitative texts into quantifiable patterns. They’re right. But a text’s linguistic operating-system deserves close scrutiny when it reveals features of the text that a human reader can’t perceive, or when it flags evidence beyond our capacity to gather. The Augmented Criticism Lab has developed algorithms to detect features of repetition and variation in the works of Shakespeare and his contemporaries (starting with drama, namely the Folger’s Digital Anthology). We’ve begun with features like rhetorical figures that repeat lemmas (heed, heedful, heeding) or morphemes (heeding, wringing, vexing). We use natural-language processing to gather evidence of these unnatural formulations, to ask whether they signal natural habits of thought. The interpretive payoff is our ability to make more definitive arguments not just about these figures, but also about underlying cognitive habits.
This paper describes our process and our corpus, and presents a range of our results with this initial corpus before we expand to the billion words in the EEBO-TCP corpus (1473-1700).
For more information about the Augmented Criticism Lab, visit < acriticismlab.org >.
I presented it first at the Canadian Society for Renaissance Studies (CSRS) conference at Congress 2015, and a revised version a few weeks later at the 2015 Shakespearean Theatre Conference in Stratford, Ontario.
My subject is “Augmented Criticism and Rhetorical Figures.” If that sounds highly technical, let me assure you that Adam and I are literary critics first and digital humanists second. That is, we use computers only to augment traditional research inquities, that are rooted in philology.
Here, for instance, our inquiry is into rhetorical figures, or the patterns of repetition and variation that make poetic language memorable, compelling, and beautiful.
This paper explores literary complexity as it manifests in rhetorical figures, or the patterns of repetition and variation that make language beautiful and memorable, and thus make it powerful. Figures have the advantage of being computationally tractable. My research team has a Python script that uses regular expressions to detect them — first in Shakespeare’s works, and then in a 400-play corpus (supplied by Martin Mueller) from 1576 to 1642. Below, I compare Shakespeare’s use of one figure to these broader habits of usage. I conclude that while Shakespeare’s use appears to be more nuanced, it is also more narrow in its ambitions.
Here’s the program that Randy Harris of the University of Waterloo has assembled for a workshop later this month on computational rhetoric, where I’ll be presenting on my Zeugmatic project and learning a lot about how great minds at Waterloo are defining rhetorical figures computationally.