The obvious thing we’re lacking.

I love Karen Coyle’s idea that we should make OCR usable by identifying the best-available copy of each text. It’s time to start thinking about this kind of thing. Digital humanists have been making big claims about our ability to interpret large collections. But — outside of a few exemplary projects like EEBO-TCP — we really don’t have free access to the kind of large, coherent collections that our rhetoric would imply. We’ve got feet of clay on this issue.

Do you think the 'ct' ligature looks a little like an ampersand? Well, so do OCR engines.

Moreover, this wouldn’t be a difficult problem to address. I think it can be even simpler than Coyle suggests. In many cases, libraries have digitized multiple copies of a single edition. The obvious, simple thing to do is just:

    Measure OCR quality — automatically, using a language model rather than ground truth — and associate a measurement of OCR quality with each bibliographic record.

This simple metric would save researchers a huge amount of labor, because a scholar could use an API to request “all the works you have between 1790 and 1820 that are above 90% probable accuracy” or “the best available copy of each edition in this period,” making it much easier to build a meaningfully normalized corpus. (This may be slightly different from Coyle’s idea about “urtexts,” because I’m talking about identifying the best copy of an edition rather than the best edition of a title.) And of course a metric destroys nothing: if you want to talk about print culture without filtering out poor OCR, all that metadata is still available. All this would do is empower researchers to make their own decisions.

One could go even further, and construct a “Frankenstein” edition by taking the best version of each page in a given edition. Or one could improve OCR with post-processing. But I think those choices can be left to individual research projects and repositories. The only part of this that really does need to be a collective enterprise is an initial measurement of OCR quality that gets associated with each bibliographic record and exposed to the API. That measurement would save research assistants thousands of hours of labor picking “the cleanest version of X.” I think it’s the most obvious thing we’re lacking.

[Postscript: Obviously, researchers can do this for themselves by downloading everything in period X, measuring OCR quality, and then selecting copies accordingly. In fact, I’m getting ready to build that workflow this summer. But this is going to take time and consume a lot of disk space, and it’s really the kind of thing an API ought to be doing for us.]

Why DH has no future.

Digital humanities is about eleven years old — counting from John Unsworth’s coinage of the phrase in 2001 — which perhaps explains why it has just discovered mortality and is anxiously contemplating its own.

Creative commons BY-NC-SA 1.0: bigadventures.

Steve Ramsay tried to head off this crisis by advising digital humanists that a healthy community “doesn’t concern itself at all with the idea that it will one day be supplanted by something else.” This was ethically wise, but about as effective as curing the hiccups by not thinking about elephants. Words like “supplant” have a way of sticking in your memory. Alex Reid then gave the discussion a twist by linking the future of DH to the uncertain future of the humanities themselves.

Meanwhile, I keep hearing friends speculate that the phrase “digital humanities” will soon become meaningless, since “everything will be digital,” and the adjective will be emptied out.

In thinking about these eschatological questions, I start from Matthew Kirschenbaum’s observation that DH is not a single intellectual project but a tactical coalition. Just for starters, humanists can be interested in digital technology a) as a way to transform scholarly communication, b) as an object of study, or c) as a means of analysis. These are distinct intellectual projects, although they happen to overlap socially right now because they all require skills and avocations that are not yet common among humanists.

This observation makes it pretty clear how “the digital humanities” will die. The project will fall apart as soon as it’s large enough for falling apart to be an option.

A) Transforming scholarly communication. This is one part of the project where I agree that “soon everyone will be a digital humanist.” The momentum of change here is clear, and there’s no reason why it shouldn’t be generalized to academia as a whole. As it does generalize, it will no longer be seen as DH.

B) Digital objects of study. It’s much less clear to me that all humanists are going to start thinking about the computational dimension of new cultural forms (videogames, recommendation algorithms, and so on). Here I would predict the classic sort of slow battle that literary modernism, for instance, had to wage in order to be accepted in the curriculum. The computational dimension of culture is going to become increasingly important, but it can’t simply displace the rest of cultural history, and not all humanists will want to acquire the algorithmic literacy required to critique it. So we could be looking at a permanent tension here, whether it ends up being a division within or between disciplines.

C) Digital means of analysis. The part of the project closest to my heart also has the murkiest future. If you forced me to speculate, I would guess that projects like text mining and digital history may remain somewhat marginal in departments of literature and history. I’m confident that we’ll build a few tools that get widely adopted by humanists; topic modeling, for instance, may become a standard way to explore large digital collections. But I’m not confident that the development of new analytical strategies will ever be seen as a central form of humanistic activity. The disciplinary loyalties of people in this subfield may also be complicated by the relatively richer funding opportunities in neighboring disciplines (like computer science).

So DH has no future, in the long run, because the three parts of DH probably confront very different kinds of future. One will be generalized; one will likely settle in for trench warfare; and one may well get absorbed by informatics. [Or become a permanent trade mission to informatics. See also Matthew Wilkens’ suggestion in the comments below. – Ed.] But personally, I’m in no rush to see any of this happen. My odds of finding a disciplinary home in the humanities will be highest as long as the DH coalition holds together; so here’s a toast to long life and happiness. We are, after all, only eleven.

[Update: In an earlier version of this post I dated Schreibman, Siemens, and Unsworth’s Companion to Digital Humanities to 2001, as Wikipedia does. But it appears that 2004 is the earliest publication date.]

[Update April 15th: I find that people are receiving this as a depressing post. But I truly didn’t mean it that way. I was trying to suggest that the projects currently grouped together as “DH” can transform the academy in a wide range of ways — ways that don’t even have to be confined to “the humanities.” So I’m predicting the death of DH only in an Obi-Wan Kenobi sense! I blame that picture of a drowned tombstone for making this seem darker than it is — a little too evocative …]

Topic modeling made just simple enough.

Right now, humanists often have to take topic modeling on faith. There are several good posts out there that introduce the principle of the thing (by Matt Jockers, for instance, and Scott Weingart). But it’s a long step up from those posts to the computer-science articles that explain “Latent Dirichlet Allocation” mathematically. My goal in this post is to provide a bridge between those two levels of difficulty.

Computer scientists make LDA seem complicated because they care about proving that their algorithms work. And the proof is indeed brain-squashingly hard. But the practice of topic modeling makes good sense on its own, without proof, and does not require you to spend even a second thinking about “Dirichlet distributions.” When the math is approached in a practical way, I think humanists will find it easy, intuitive, and empowering. This post focuses on LDA as shorthand for a broader family of “probabilistic” techniques. I’m going to ask how they work, what they’re for, and what their limits are.

How does it work? Say we’ve got a collection of documents, and we want to identify underlying “topics” that organize the collection. Assume that each document contains a mixture of different topics. Let’s also assume that a “topic” can be understood as a collection of words that have different probabilities of appearance in passages discussing the topic. One topic might contain many occurrences of “organize,” “committee,” “direct,” and “lead.” Another might contain a lot of “mercury” and “arsenic,” with a few occurrences of “lead.” (Most of the occurrences of “lead” in this second topic, incidentally, are nouns instead of verbs; part of the value of LDA will be that it implicitly sorts out the different contexts/meanings of a written symbol.)

The assumptions behind topic modeling.
Of course, we can’t directly observe topics; in reality all we have are documents. Topic modeling is a way of extrapolating backward from a collection of documents to infer the discourses (“topics”) that could have generated them. (The notion that documents are produced by discourses rather than authors is alien to common sense, but not alien to literary theory.) Unfortunately, there is no way to infer the topics exactly: there are too many unknowns. But pretend for a moment that we had the problem mostly solved. Suppose we knew which topic produced every word in the collection, except for this one word in document D. The word happens to be “lead,” which we’ll call word type W. How are we going to decide whether this occurrence of W belongs to topic Z?

We can’t know for sure. But one way to guess is to consider two questions. A) How often does “lead” appear in topic Z elsewhere? If “lead” often occurs in discussions of Z, then this instance of “lead” might belong to Z as well. But a word can be common in more than one topic. And we don’t want to assign “lead” to a topic about leadership if this document is mostly about heavy metal contamination. So we also need to consider B) How common is topic Z in the rest of this document?

Here’s what we’ll do. For each possible topic Z, we’ll multiply the frequency of this word type W in Z by the number of other words in document D that already belong to Z. The result will represent the probability that this word came from Z. Here’s the actual formula:

Simple enough. Okay, yes, there are a few Greek letters scattered in there, but they aren’t terribly important. They’re called “hyperparameters” — stop right there! I see you reaching to close that browser tab! — but you can also think of them simply as fudge factors. There’s some chance that this word belongs to topic Z even if it is nowhere else associated with Z; the fudge factors keep that possibility open. The overall emphasis on probability in this technique, of course, is why it’s called probabilistic topic modeling.

Now, suppose that instead of having the problem mostly solved, we had only a wild guess which word belonged to which topic. We could still use the strategy outlined above to improve our guess, by making it more internally consistent. We could go through the collection, word by word, and reassign each word to a topic, guided by the formula above. As we do that, a) words will gradually become more common in topics where they are already common. And also, b) topics will become more common in documents where they are already common. Thus our model will gradually become more consistent as topics focus on specific words and documents. But it can’t ever become perfectly consistent, because words and documents don’t line up in one-to-one fashion. So the tendency for topics to concentrate on particular words and documents will eventually be limited by the actual, messy distribution of words across documents.

That’s how topic modeling works in practice. You assign words to topics randomly and then just keep improving the model, to make your guess more internally consistent, until the model reaches an equilibrium that is as consistent as the collection allows.

What is it for? Topic modeling gives us a way to infer the latent structure behind a collection of documents. In principle, it could work at any scale, but I tend to think human beings are already pretty good at inferring the latent structure in (say) a single writer’s oeuvre. I suspect this technique becomes more useful as we move toward a scale that is too large to fit into human memory.

So far, most of the humanists who have explored topic modeling have been historians, and I suspect that historians and literary scholars will use this technique differently. Generally, historians have tried to assign a single label to each topic. So in mining the Richmond Daily Dispatch, Robert K. Nelson looks at a topic with words like “hundred,” “cotton,” “year,” “dollars,” and “money,” and identifies it as TRADE — plausibly enough. Then he can graph the frequency of the topic as it varies over the print run of the newspaper.

As a literary scholar, I find that I learn more from ambiguous topics than I do from straightforwardly semantic ones. When I run into a topic like “sea,” “ship,” “boat,” “shore,” “vessel,” “water,” I shrug. Yes, some books discuss sea travel more than others do. But I’m more interested in topics like this:

You can tell by looking at the list of words that this is poetry, and plotting the volumes where the topic is prominent confirms the guess.

This topic is prominent in volumes of poetry from 1815 to 1835, especially in poetry by women, including Felicia Hemans, Letitia Landon, and Caroline Norton. Lord Byron is also well represented. It’s not really a “topic,” of course, because these words aren’t linked by a single referent. Rather it’s a discourse or a kind of poetic rhetoric. In part it seems predictably Romantic (“deep bright wild eye”), but less colorful function words like “where” and “when” may reveal just as much about the rhetoric that binds this topic together.

A topic like this one is hard to interpret. But for a literary scholar, that’s a plus. I want this technique to point me toward something I don’t yet understand, and I almost never find that the results are too ambiguous to be useful. The problematic topics are the intuitive ones — the ones that are clearly about war, or seafaring, or trade. I can’t do much with those.

Now, I have to admit that there’s a bit of fine-tuning required up front, before I start getting “meaningfully ambiguous” results. In particular, a standard list of stopwords is rarely adequate. For instance, in topic-modeling fiction I find it useful to get rid of at least the most common personal pronouns, because otherwise the difference between 1st and 3rd person point-of-view becomes a dominant signal that crowds out other interesting phenomena. Personal names also need to be weeded out; otherwise you discover strong, boring connections between every book with a character named “Richard.” This sort of thing is very much a critical judgment call; it’s not a science.

I should also admit that, when you’re modeling fiction, the “author” signal can be very strong. I frequently discover topics that are dominated by a single author, and clearly reflect her unique idiom. This could be a feature or a bug, depending on your interests; I tend to view it as a bug, but I find that the author signal does diffuse more or less automatically as the collection expands.

Topic prominently featuring Austen.
What are the limits of probabilistic topic modeling? I spent a long time resisting the allure of LDA, because it seemed like a fragile and unnecessarily complicated technique. But I have convinced myself that it’s both effective and less complex than I thought. (Matt Jockers, Travis Brown, Neil Fraistat, and Scott Weingart also deserve credit for convincing me to try it.)

This isn’t to say that we need to use probabilistic techniques for everything we do. LDA and its relatives are valuable exploratory methods, but I’m not sure how much value they will have as evidence. For one thing, they require you to make a series of judgment calls that deeply shape the results you get (from choosing stopwords, to the number of topics produced, to the scope of the collection). The resulting model ends up being tailored in difficult-to-explain ways by a researcher’s preferences. Simpler techniques, like corpus comparison, can answer a question more transparently and persuasively, if the question is already well-formed. (In this sense, I think Ben Schmidt is right to feel that topic modeling wouldn’t be particularly useful for the kinds of comparative questions he likes to pose.)

Moreover, probabilistic techniques have an unholy thirst for memory and processing time. You have to create several different variables for every single word in the corpus. The models I’ve been running, with roughly 2,000 volumes, are getting near the edge of what can be done on an average desktop machine, and commonly take a day. To go any further with this, I’m going to have to beg for computing time. That’s not a problem for me here at Urbana-Champaign (you may recall that we invented HAL), but it will become a problem for humanists at other kinds of institutions.

Probabilistic methods are also less robust than, say, vector-space methods. When I started running LDA, I immediately discovered noise in my collection that had not previously been a problem. Running headers at the tops of pages, in particular, left traces: until I took out those headers, topics were suspiciously sensitive to the titles of volumes. But LDA is sensitive to noise, after all, because it is sensitive to everything else! On the whole, if you’re just fishing for interesting patterns in a large collection of documents, I think probabilistic techniques are the way to go.

Where to go next
The standard implementation of LDA is the one in MALLET. I haven’t used it yet, because I wanted to build my own version, to make sure I understood everything clearly. But MALLET is better. If you want a few examples of complete topic models on collections of 18/19c volumes, I’ve put some models, with R scripts to load them, in my github folder.

If you want to understand the technique more deeply, the first thing to do is to read up on Bayesian statistics. In this post, I gloss over the Bayesian underpinnings of LDA because I think the implementation (using a strategy called Gibbs sampling, which is actually what I described above!) is intuitive enough without them. And this might be all you need! I doubt most humanists will need to go further. But if you do want to tinker with the algorithm, you’ll need to understand Bayesian probability.

David Blei invented LDA, and writes well, so if you want to understand why this technique has “Dirichlet” in its name, his works are the next things to read. I recommend his Introduction to Probabilistic Topic Models. It recently came out in Communications of the ACM, but I think you get a more readable version by going to his publication page (link above) and clicking the pdf link at the top of the page.

Probably the next place to go is “Rethinking LDA: Why Priors Matter,” a really thoughtful article by Hanna Wallach, David Mimno, and Andrew McCallum that explains the “hyperparameters” I glossed over in a more principled way.

Then there are a whole family of techniques related to LDA — Topics Over Time, Dynamic Topic Modeling, Hierarchical LDA, Pachinko Allocation — that one can explore rapidly enough by searching the web. In general, it’s a good idea to approach these skeptically. They all promise to do more than LDA does, but they also add additional assumptions to the model, and humanists are going to need to reflect carefully about which assumptions we actually want to make. I do think humanists will want to modify the LDA algorithm, but it’s probably something we’re going to have to do for ourselves; I’m not convinced that computer scientists understand our problems well enough to do this kind of fine-tuning.

What kinds of “topics” does topic modeling actually produce?

I’m having an interesting discussion with Lisa Rhody about the significance of topic modeling at different scales that I’d like to follow up with some examples.

I’ve been doing topic modeling on collections of eighteenth- and nineteenth-century volumes, using volumes themselves as the “documents” being modeled. Lisa has been pursuing topic modeling on a collection of poems, using individual poems as the documents being modeled.

The math we’re using is probably similar. I believe Lisa is using MALLET. I’m using a version of Latent Dirichlet Allocation that I wrote in Java so I could tinker with it.

But the interesting question we’re exploring is this: How does the meaning of LDA change when it’s applied to writing at different scales of granularity? Lisa’s documents (poems) are a typical size for LDA: this technique is often applied to identify topics in newspaper articles, for instance. This is a scale that seems roughly in keeping with the meaning of the word “topic.” We often assume that the topic of written discourse changes from paragraph to paragraph, “topic sentence” to “topic sentence.”

By contrast, I’m using documents (volumes) that are much larger than a paragraph, so how is it possible to produce topics as narrowly defined as this one?

This is based on a generically diverse collection of 1,782 19c volumes, not all of which are plotted here (only the volumes where the topic is most prominent are plotted; the gray line represents an aggregate frequency including unplotted volumes). The most prominent words in this topic are “mother, little, child, children, old, father, poor, boy, young, family.” It’s clearly a topic about familial relationships, and more specifically about parent-child relationships. But there aren’t a whole lot of books in my collection specifically about parent-child relationships! True, the most prominent books in the topic are A. F. Chamberlain’s The Child and Childhood in Folk Thought (1896) and Alice Earl Morse’s Child Life in Colonial Days (1899), but most of the rest of the prominent volumes are novels — by, for instance, Catharine Sedgwick, William Thackeray, Louisa May Alcott, and so on. Since few novels are exclusively about parent-child relations, how can the differences between novels help LDA identify this topic?

The answer is that the LDA algorithm doesn’t demand anything remotely like a one-to-one relationship between documents and topics. LDA uses the differences between documents to distinguish topics — but not by establishing a one-to-one mapping. On the contrary, every document contains a bit of every topic, although it contains them in different proportions. The numerical variation of topic proportions between documents provides a kind of mathematical leverage that distinguishes topics from each other.

The implication of this is that your documents can be considerably larger than the kind of granularity you’re trying to model. As long as the documents are small enough that the proportions between topics vary significantly from one document to the next, you’ll get the leverage you need to discriminate those topics. Thus you can model a collection of volumes and get topics that are not mere “subject classifications” for volumes.

Now, in the comments to an earlier post I also said that I thought “topic” was not always the right word to use for the categories that are produced by topic modeling. I suggested that “discourse” might be better, because topics are not always unified semantically. This is a place where Lisa starts to question my methodology a little, and I don’t blame her for doing so; I’m making a claim that runs against the grain of a lot of existing discussion about “topic modeling.” The computer scientists who invented this technique certainly thought they were designing it to identify semantically coherent “topics.” If I’m not doing that, then, frankly, am I using it right? Let’s consider this example:

This is based on the same generically diverse 19c collection. The most prominent words are “love, life, soul, world, god, death, things, heart, men, man, us, earth.” Now, I would not call that a semantically coherent topic. There is some religious language in there, but it’s not about religion as such. “Love” and “heart” are mixed in there; so are “men” and “man,” “world” and “earth.” It’s clearly a kind of poetic diction (as you can tell from the color of the little circles), and one that increases in prominence as the nineteenth century goes on. But you would be hard pressed to identify this topic with a single concept.

Does that mean topic modeling isn’t working well here? Does it mean that I should fix the system so that it would produce topics that are easier to label with a single concept? Or does it mean that LDA is telling me something interesting about Victorian poetry — something that might be roughly outlined as an emergent discourse of “spiritual earnestness” and “self-conscious simplicity”? It’s an open question, but I lean toward the latter alternative. (By the way, the writers most prominently involved here include Christina Rossetti, Algernon Swinburne, and both Brownings.)

In an earlier comment I implied that the choice between “semantic” topics and “discourses” might be aligned with topic modeling at different scales, but I’m not really sure that’s true. I’m sure that the document size we choose does affect the level of granularity we’re modeling, but I’m not sure how radically it affects it. (I believe Matt Jockers has done some systematic work on that question, but I’ll also be interested to see the results Lisa gets when she models differences between poems.)

I actually suspect that the topics identified by LDA probably always have the character of “discourses.” They are, technically, “kinds of language that tend to occur in the same discursive contexts.” But a “kind of language” may or may not really be a “topic.” I suspect you’re always going to get things like “art hath thy thou,” which are better called a “register” or a “sociolect” than they are a “topic.” For me, this is not a problem to be fixed. After all, if I really want to identify topics, I can open a thesaurus. The great thing about topic modeling is that it maps the actual discursive contours of a collection, which may or may not line up with “concepts” any writer ever consciously held in mind.

Computer scientists don’t understand the technique that way.* But on this point, I think we literary scholars have something to teach them.

On the collective course blog for English 581 I have some other examples of topics produced at a volume level.

*[UPDATE April 3, 2012: Allen Riddell rightly points out in the comments below that Blei’s original LDA article is elegantly agnostic about the significance of the “topics” — which are at bottom just “latent variables.” The word “topic” may be misleading, but computer scientists themselves are often quite careful about interpretation.]

Documentation / open data:
I’ve put the topic model I used to produce these visualizations on github. It’s in the subfolder 19th150topics under folder BrowseLDA. Each folder contains an R script that you run; it then prompts you to load the data files included in the same folder, and allows you to browse around in the topic model, visualizing each topic as you go.

I have also pushed my Java code for LDA up to github. But really, most people are better off with MALLET, which is infinitely faster and has hyperparameter optimization that I haven’t added yet. I wrote this just so that I would be able to see all the moving parts and understand how they worked.