There has been some recent discussion on Twitter in response to Alan Liu’s call for model corpora for students. It’s an extremely exciting conversation that’s long overdue. We need better model corpora. And I think it’s important for DH scholars to recognize that this is not just a pedagogical requirement — it’s a requirement for research at every level.1
Consider the field of machine learning. Almost anyone who has ever done research in the field has heard of the MNIST database. It’s a collection of 60,000 handwritten numerical digits, labeled with their actual values, preprocessed for ease of use, and carefully designed to pose a substantial challenge for learning algorithms. Achieving a decent error rate isn’t too hard, but achieving a very low error rate seems to require things like 6-layer feed-forward neural networks with thousands of neurons in each layer.
What’s great about the MNIST database is that many, many people have used it to test their work. Now, whenever you try a new learning algorithm, you can toss the MNIST data at it. If your algorithm achieves a reasonable error rate, you can feel pretty confident that you’re not making some kind of gross error, and that your algorithm really is recognizing handwriting, rather than noticing and generalizing from irrelevant details. Unfamiliar data could have surprising biases or unexpected patterns that throw off the performance of your algorithm, but the MNIST database is carefully curated to avoid many such problems. That’s vital for teaching, but it’s just as vital for research.
Andrew Ng compares this to the use of model organisms in biology. Mice, for example, consume few resources, develop and reproduce quickly, and share fundamental biological traits with many mammals, including humans. For those reasons, people have been studying them for a very long time and their biology is well understood. Researchers who want to study mammals, and who are already committed to making the kinds of ethical trade-offs that animal experimentation entails, will almost certainly start with mice or other related model organisms. The epistemological, practical, and ethical benefits are manifold. There will be fewer ways for the work to go wrong in unexpected ways, the research will proceed more efficiently, and fewer animals will suffer overall.
Fortunately, digital humanists don’t face the same ethical questions as biologists. Our “mouse models” can consist of nothing but data. But we still don’t have enough of them.2
I found the absence particularly frustrating as I sat down to play with Syuzhet a couple of weeks ago. I was initially at a loss about what data to use. It quickly occurred to me that I ought to start with Romeo and Juliet because that’s what other researchers had used, and for good reason. It’s familiar to a broad range of audiences, so it’s easy to get data about it from actual human beings. It has large variations in emotional valence with relatively clear trajectories. And well, you know — it’s Shakespeare. But one play by one author isn’t really good enough. What we need is a model corpus — or rather, many model corpora from different periods, in different genres, different languages, different registers, and so on.
There has been some discussion about how to construct corpora that are representative, but in these discussions, the question is often about whether the corpus gives us access to some kind of ground truth about the culture from which it is sampled.3 That’s an extremely important question — one of the most important in the digital humanities. But I worry that we’re not quite ready to begin answering it. We don’t know whether corpora are representative, but we also don’t know for sure what tools to use in our investigations. And it’s going to be difficult to refine our tools and build new representative corpora at the same time. In our rush to take advantage of the sudden ubiquity of literary and historical data, we might be skipping a step. We need to understand the tools we use, and to understand the tools we use, we need to test them on corpora that we understand.4
From one perspective, this is a matter of validation — of checking new results against what we already know.5 But it’s a different kind of validation than many of us are used to — where by “us” I mean mostly literary scholars, but possibly other humanists as well. It doesn’t ask “is this a claim that comports with known evidence?” It asks “what is the precise meaning of this claim?” This second question becomes important when we use an unfamiliar tool to make a claim; we need to understand the claim itself before saying whether it comports with known evidence.
From another perspective, this is a matter of theorization — of clarifying assumptions, developing conceptual structures, and evaluating argumentative protocols. But it’s a different kind of theory than many of us are used to. It doesn’t ask “what can we learn by troubling the unspoken assumptions that make this or that interpretation seem obvious?” It asks “how can we link the representations these tools produce to familiar concepts?” Literary theory has often involved questioning the familiar by setting it at a distance. But distant reading already does that — there are few obvious interpretations in the field right now, and theory may need to play a slightly different role than it has in previous decades.
From either perspective, the point of a model corpus would not be to learn about the texts in the corpus, or about the culture that produced those texts. It would be to learn about the tools that we use on that corpus, about the claims those tools might support, and about the claims they cannot support.
But what should a model corpus look like? This is where I become less certain. My first instinct is to say “let’s look at what corpus linguists do.” But the kinds of questions linguists are likely to ask are very different from the ones that literary scholars are likely to ask. Still, there are some great starting points, including a remarkably comprehensive list from Richard Xiao. Among those, the ARCHER corpus seems particularly promising. (Thanks to Heather Froelich for these suggestions!)
But in the long run, we’ll want to produce our own corpora. Fortunately, Alan Liu has already given this some thought! His focus is on pedagogical issues, but again, the kinds of model corpora he talks about are vital for research as well. On Twitter, he offered a brilliant enumeration of desirable qualities such corpora would have. I’m reproducing it here, lightly paraphrased:
Imagine what a ready-for-student-use corpus of literary materials would look like. Specs include the following:
- Free and public domain.
- Of manageable size (e.g., low thousands and not hundreds of thousands of items).
- Modular by nation, genre, period, language.
- Socioculturally diverse.
- Richly annotated with metadata.
- Pre-cleaned and chunked (or packaged with easy-to-use processing scripts).
- Compatible in format with similar corpora of materials in history and other fields (to encourage cross-domain experiments in analysis).
- Matched by period and nation to available linguistic corpora that can be used as reference corpora.
I think this is a terrific set of initial goals for model corpora, both for researchers and students. We’ll need to keep having conversations about requirements, and of course no one corpus will serve all our needs. Different disciplines within the humanities will have different requirements. But it’s clear to me that if digital humanists can develop a “canon” of familiar corpora useful for validating new tools, the field will have taken a significant step forward.
Let’s get started!
Since there are already several links to helpful resources for thinking about humanistic corpora, I’m going to start a corpus creation and curation bibliography here. This will probably graduate into its own page or post.
- Richard Xiao, Well-known and influential corpora
- Miriam Posner, Humanities Data: A Necessary Contradiction.
- Douglas Biber, Representativeness in Corpus Design
- Multiple Corpora via corpus.byu.edu. Highlights:
- Varieng’s Corpus Finder
- A Google Search for “Corpus Creation.” (To be expanded into actual sources. Upon revisiting this page, I have realized that Heather Froelich was offering a very gentle version of this. Sorry Heather!)
- Update: Since posting this, I’ve learned that Laura Mandell, in collaboration with the NovelTM partnership, is working on a proposal for a journal dedicated to digital humanities corpora. I think this will be a fantastic development for the field! ↩
- There are some examples — such as the much-studied Federalist papers, which might be a good dataset to consider for testing new approaches to authorship attribution. And of course there are numerous standard corpora for use by corpus linguists — the Brown, AP, and Wall Street Journal corpora come to mind for American English, and there are many others. But these corpora have not been selected with literary studies in mind! This is where I parade my ignorance in the hope that someone will enlighten me: are there other corpora designed to be of specific methodological interest to literary scholars? ↩
- Most recently, Scott Weingart took up the question of corpus representativeness in his discussion of the Great Tsundoku, focusing on questions about what was written, what was published, and what was read. He also noted a conversation from a few years ago that Ted Underwood documented, and drew attention to Heather Froelich, who does lots of careful thinking about issues like this. And needless to say, Franco Moretti was thinking about this long ago. I think we’ll be asking this question in different ways for another fifty years. ↩
- Initially I said “We need to understand the tools we use first,” but that’s not quite right either. There’s a cyclical dependency here! ↩
- This has been a topic of widespread interest after the recent Syuzhet conversation, and I think the kinds of collective validation that Andrew Piper and others have called for would be made vastly easier by a somewhat standardized set of model corpora familiar to many researchers. ↩