The Augmented Criticism Lab’s Sonnet Database

This is the text of a short paper I delivered at the Digital Humanities 2019 conference in Utrecht, the Netherlands on 12 July 2019. The Augmented Criticism Lab’s Sonnet Database is in beta release.

To keep to my 10 minutes, I’ll be as focused as possible. My aim is to raise a research question, and then to describe my methods for answering it.

My question is:

Is the sonnet a form or a genre?

My method for answering it is:

A database of sonnets for text-analysis.

Continue reading “The Augmented Criticism Lab’s Sonnet Database”

John Donne and the Sonnet Problem


What makes a sonnet? For most early modern examples, the answer is clear: a 14-line rhyming poem, its form either Shakespearean (three quatrains and a couplet) or Petrarchan (an octave and a sestet). There are exceptions to those formal rules, but most sonnets meet them.

Formal rules are the conventional answer. And that answer works for conventional sonnets, which are the vast majority of sonnets.

But if you enforce formal rules too rigorously, you encounter a few interesting problems. These are the problems that my project is investigating. Moments’ Monuments: The ACL Database is collecting as many sonnets as possible, so I can get a more definitive answer to this question: Is the sonnet a form or a genre? The trouble is, you need to decide first what qualifies as a sonnet.

Continue reading “John Donne and the Sonnet Problem”

NLP for Literary Critics: An Introduction and Tutorial

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?
Continue reading “NLP for Literary Critics: An Introduction and Tutorial”

Get with the Programming

(This continues my previous post on this research project, about my questions and initial steps.)

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.

Okay, so my program just prepares my text files to run a far more complex program by Marie Dubremetz at Uppsala University (chiasmusDetector), but getting it to run on my files took some work.

Continue reading “Get with the Programming”

Find all the Figures

What?

“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. Continue reading “Find all the Figures”

What can Machine Learning do for Literary Critics?

 First in a series of posts about artificial intelligence sparked by “The Great AI Awakening,” an article from December 2016 by Gideon Lewis-Kraus in the New York Times Magazine. Cross-posted to The Augmented Criticism Lab‘s blog.

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. Continue reading “What can Machine Learning do for Literary Critics?”

Talk: Unnatural Language and Natural Thinking

I’m giving a talk on the University of Calgary campus (in SS 1015) on Friday December 2, 2016 at 3:15pm.

Title

Unnatural Language and Natural Thinking: Shakespeare and His Contemporaries

Abstract

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 >.