18c 19c linguistics

“… a selection of the language really spoken by men”?

William Wordsworth’s claim to have brought poetry back to “the language of conversation in the middle and lower classes of society” gets repeated to each new generation of students (1). But did early nineteenth-century writing in general become more accessible, or closer to speech? It’s hard to say. We’ve used remarks like Wordsworth’s to anchor literary history, but we haven’t had a good way to assess their representativeness.

Increasingly, though, we’re in a position to test some familiar stories about literary history — to describe how the language of one genre changed relative to others, or even relative to “the language of conversation.” We don’t have eighteenth-century English speakers to interview, but we do have evidence about the kinds of words that tend to be more common in spoken language. For instance, Laly Bar-Ilan and Ruth Berman have shown in the journal Linguistics that contemporary spoken English is distinguished from writing by containing a higher proportion of words from the Old English part of the lexicon (2). This isn’t terribly surprising, since English was for a couple of hundred years (1066-1250) almost exclusively a spoken language, while French and Latin were used for writing. Any word that entered English before this period, and survived, had to be the kind of word that gets used in conversation. Words that entered afterward were often borrowed from French or Latin to flesh out the written language.

If the spoken language was distinguished from writing this way in the thirteenth century, and the same thing holds true today, then one might expect it to hold true in the eighteenth and nineteenth centuries as well. And it does seem to hold true: eighteenth-century drama, written to be spoken on stage, is distinguished from nondramatic poetry and prose by containing a higher proportion of Old English words. This is a broad-brush approach to diction, and not one that I would use to describe individual works. But applied to an appropriately large canvas, it may give us a rough picture of how the “register” of written diction has changed across time, becoming more conversational or more formal.

This graph is based on a version of the Google English corpus that I’ve cleaned up in a number of ways. Common OCR errors involving s, f, and ct have been corrected. The graph shows the aggregate frequency of the 500 most common English words that entered the language before the twelfth century. (I’ve found date-of-entry a more useful metric of a word’s affinity with spoken language than terms like “Latinate” or “Germanic.” After all, “Latinate” words like “school,” “street,” and “wall” don’t feel learned to us, because they’ve been transmitted orally for more than a millennium.) I’ve excluded a list of stopwords that includes determiners, prepositions, pronouns, and conjunctions, as well as the auxiliary verbs “be,” “will,” and “have.”

In relative terms, the change here may not look enormous; the peak in the early eighteenth century (181 words per thousand) is only about 20% higher than the trough in the late eighteenth century (152 words per thousand). But we’re talking about some of the most common words in the language (can, think, do, self, way, need, know). It’s a bit surprising that this part of the lexicon fluctuates at all. You might expect to see a gradual decline in the frequency of these words, as the overall size of the lexicon increases. But that’s not what happens: instead we see a rapid decline in the eighteenth century (as prose becomes less like speech, or at least less like the imagined speech of contemporaneous drama), and then a gradual recovery throughout the nineteenth century.

What does this tell us about literature? Not much, without information about genre. After all, as I mentioned, dramatic writing is a lot closer to speech than, say, poetry is. This curve might just be telling us that very few plays got written in the late eighteenth century.

Fortunately it’s possible to check the Google corpus against a smaller corpus of individual texts categorized by genre. I’ve made an initial pass at the first hundred years of this problem using a corpus of 2,188 eighteenth-century books produced by ECCO-TCP, which I obtained in plain text with help from Laura Mandell and 18thConnect. Two thousand books isn’t a huge corpus, especially not after you divide them up by genre, so these results are only preliminary. But the initial results seem to confirm that the change involved the language of prose itself, and not just changes in the relative prominence of different genres. Both fiction and nonfiction prose show a marked change across the century. If I’m right that the frequency of pre-12c words is a fair proxy for resemblance to spoken language, they became less and less like speech.

“Fiction” is of course a fuzzy category in the eighteenth century. The blurriness of the boundary between a sensationalized biography and a “novel” is a lot of the point of being a novel. In the graph above, I’ve lumped biographies and collections of personal letters in with novels, because I’m less interested in distinguishing something unique about fiction than I am in confirming a broad change in the diction of nondramatic prose.

By contrast, there’s relatively little change in the diction of poetry and drama. The proportion of pre-twelfth-century words is roughly the same at the end of the century as it was at the beginning.

Are these results intuitive, or are they telling us something new? I think the general direction of these curves probably confirms some intuitions. Anyone who studies eighteenth and nineteenth-century English knows that you get a lot of long words around 1800. Sad things become melancholy, needs become a necessity, and so on.

What may not be intuitive is how broad and steady the arc of change appears to be. To the extent that we English professors have any explanation for the elegant elaboration of late-eighteenth-century prose, I think we tend to blame Samuel Johnson. But these graphs suggest that much of the change had already taken place by the time Johnson published his Dictionary. Moreover, our existing stories about the history of style put a lot of emphasis on poetry — for instance, on Wordsworth’s critique of poetic diction. But the biggest changes in the eighteenth century seem to have involved prose rather than poetry. It’ll be interesting to see whether that holds true in the nineteenth century as well.

How do we explain these changes? I’m still trying to figure out. In the next couple of weeks I’ll write a post asking what took up the slack: what kinds of language became common in books where old, common words were relatively underrepresented?

—– references —–
1) William Wordsworth and Samuel T. Coleridge, Lyrical Ballads, with a Few Other Poems (Bristol: 1798), i.
2) Laly Bar-Ilan and Ruth A. Berman, “Developing register differentiation: the Latinate-Germanic divide in English,” Linguistics 45 (2007): 1-35.

18c 19c methodology

Using clustering to explore the relationships between trending topics.

Until recently, I’ve been limited to working with tools provided by other people. But in the last month or so I realized that it’s easier than it used to be to build these things yourself, so I gave myself a crash course in MySQL and R, with a bit of guidance provided by Ben Schmidt, whose blog Sapping Attention has been a source of many good ideas. I should also credit Matt Jockers and Ryan Heuser at the Stanford Literary Lab, who are doing fabulous work on several different topics; I’m learning more from their example than I can say here, since I don’t want to describe their research in excessive detail.

I’ve now been able to download Google’s 1gram English corpus between 1700 and 1899, and have normalized it to make it more useful for exploring the 18th and 19th centuries. In particular, I normalized case and developed a way to partly correct for the common OCR errors that otherwise make the Google corpus useless in the eighteenth century: especially the substitutions s->f and ss->fl.

Having done that, I built a couple of modules that mine the dataset for patterns. Last December, I was intrigued to discover that words with close semantic relationships tend to track each other closely (using simple sensory examples like the names of colors and oppositions like hot/cold). I suspected that this pattern might extend to more abstract concepts as well, but it’s difficult to explore that hypothesis if you have to test possible instances one by one. The correlation-seeking module has made it possible to explore it more rapidly, and has also put some numbers on what was before a purely visual sense of “fittedness.”

For instance, consider “diction.” It turns out that the closest correlate to “diction” in the period 1700-1899 is “versification,” which has a Pearson correlation coefficient of 0.87. (If this graph doesn’t seem to match the Google version, remember that the ngram viewer is useless in the 18c until you correct for case and long s.)
diction, versification, in the Google English corpus, 1700-1899
The other words that correlate most closely with “diction” are all similarly drawn from the discourse of poetic criticism. “Poem” and “stanzas” have a coefficient of 0.82; “poetical” is 0.81. It’s a bit surprising that correlation of yearly frequencies should produce such close thematic connections. Obviously, a given subject category will be overrepresented in certain years, and underrepresented in others, so thematically related words will tend to vary together. But in a corpus as large and diverse as Google’s, one might have expected that subtle variation to be swamped by other signals. In practice it isn’t.

I’ve also built a module that looks for words that are overrepresented in a given period relative to the rest of 1700-1899. The measures of overrepresentation I’m using are a bit idiosyncratic. I’m simply comparing the mean frequency inside the period to the mean frequency outside it. I take the natural log of the absolute difference between those means, and multiply it by the ratio (frequency in the period/frequency outside it). For the moment, that formula seems to be working; I’ll try other methods (log-likelihood, etc.) later on.

Once I find a list of, say, fifty words that are overrepresented in a period, I can generate a correlation matrix based on their correlations with each other, and then do hierarchical clustering on that matrix to reveal which words track each other most closely. In effect, I create a broad list of “trending topics” in a particular period, and then use a more precise sort of curve-matching to define the relationships between those trends.

One might imagine that matching words on the basis of change-across-time would be a blunt instrument compared to a more intimate approach based on collocation in the same sentences, or at least co-occurrence in the same volumes. And for many purposes that will be true. But I’ve found that my experiments with smaller-scale co-occurrence (e.g. in MONK) often lead me into tautological dead ends. I’ll discover, e.g., that historical novels share the kind of vocabulary I sort of knew historical novels were likely to share. Relying on yearly frequency data makes it easier to avoid those dead ends, because they have the potential to turn up patterns that aren’t based purely on a single familiar genre or subject category. They may be a blunt instrument, but through their very bluntness they allow us to back up to a vantage point where it’s possible to survey phenomena that are historical rather than purely semantic.

I’ve included an example below. The clusters that emerge here are based on a kind of semantic connection, but often it’s a connection that only makes sense in the context of the period. For instance, “nitrous” and “inflammable” may seem a strange pairing, unless you know that the recently-discovered gas hydrogen was called “inflammable air,” and that chemists were breathing nitrous oxide, aka laughing gas. “Sir” and “de” may seem a strange pairing, unless you reflect that “de” is a French particle of nobility analogous to “sir,” and so on. But I also find that I’m discovering a lot here I didn’t previously know. For instance, I probably should have guessed that Petrarch was a big deal in this period, since there was a sonnet revival — but that’s not something I actually knew, and it took me a while to figure out why Petrarch was coming up. I still don’t know why he’s connected to the dramatist Charles Macklin.

Trending topics in the period 1775-1825.
There are lots of other fun pairings in there, especially britain/commerce/islands and the group of flashy hyperbolic adverbs totally/frequently/extremely connected to elegance/opulence. I’m not sure that I would claim a graph like this has much evidentiary value; clustering algorithms are sensitive to slight shifts in initial conditions, so a different list of words might produce different groupings. But I’m also not sure that evidentiary value needs to be our goal. Lately I’ve been inclined to argue that the real utility of text mining may be as a heuristic that helps us discover puzzling questions. I certainly feel that a graph like this helps me identify topics (and more subtly, kinds of periodized diction) that I didn’t recognize before, and that deserve further exploration. [UPDATE 4/20/2011: Back in February I was doing this clustering with yearly frequency data, and Pearson’s correlation, which worked surprisingly well. But I’m now fairly certain that it’s better to do it with co-occurrence data, and a vector space model. See this more recent post.]


A bit more on the tension between heuristic and evidentiary methods.

Just a quick link to this post at cliotropic, (h/t Dan Cohen) which dramatizes what’s concretely at stake in the tension I was describing earlier between heuristic and evidentiary applications of technology.

Shane Landrum reports that historians on the job market may run into skeptical questions from social scientists — who apparently don’t like to see visualization used as a heuristic. They call it “fishing for a thesis.”

I think I understand the source of the tension here. In a discipline focused on the present, where evidence can be produced essentially at will, a primary problem that confronts researchers is that you can prove anything if you just keep rolling the dice often enough. “Fishing expeditions” really are a problem for this kind of enterprise, because there’s always going to be some sort of pattern in your data. If you wait to define a thesis until you see what patterns emerge, then you’re going to end up crafting a thesis to fit what might be an accidental bounce of the dice in a particular experiment.

Obviously history and literary studies are engaged in a different sort of enterprise, because our archives are for the most part fixed. We occasionally discover new documents, but history as a whole isn’t an experiment we can repeat, so we’re not inclined to view historical patterns as things that “might not have statistical significance.” I mean, of course in a sense all historical patterns may have been accidents. But if they happened, they’re significant — the question of whether they would happen again if we repeated the experiment isn’t one that we usually spend much time debating. So “fishing for patterns” isn’t usually something that bothers us; in fact, we’re likely to value heuristics that help us discover them.

18c 19c methodology

How you were tricked into doing text mining.

It’s only in the last few months that I’ve come to understand how complex search engines actually are. Part of the logic of their success has been to hide the underlying complexity from users. We don’t have to understand how a search engine assigns different statistical weights to different terms; we just keep adding terms until we find what we want. The differences between algorithms (which range widely in complexity, and are based on different assumptions about the relationship between words and documents) never cross our minds.

Card catalogs at Sterling Memorial Library, Yale. Image courtesy Wikimedia commons.

I’m pretty sure search engines have transformed literary scholarship. There was a time (I can dimly recall) when it was difficult to find new primary sources. You had to browse through a lot of bibliographies looking for an occasional lead. I can also recall the thrill I felt, at some point in the 90s, when I realized that full-text searches in a Chadwyck-Healey database could rapidly produce a much larger number of leads — things relevant to my topic, that no one else seemed to have read. Of course, I wasn’t the only one realizing this. I suspect the wave of challenges to canonical “Romanticism” in the 90s had a lot to do with the fact that Romanticists all realized, around the same time, that we had been looking at the tip of an iceberg.

One could debate whether search engines have exerted, on balance, a positive or negative influence on scholarship. If the old paucity of sources tempted critics to endlessly chew over a small and unrepresentative group of authors, the new abundance may tempt us to treat all works as more or less equivalent — when perhaps they don’t all provide the same kinds of evidence. But no one blames search engines themselves for this, because search isn’t an evidentiary process. It doesn’t claim to prove a thesis. It’s just a heuristic: a technique that helps you find a lead, or get a better grip on a problem, and thus abbreviates the quest for a thesis.

The lesson I would take away from this story is that it’s much easier to transform a discipline when you present a new technique as a heuristic than when you present it as evidence. Of course, common sense tells us that the reverse is true. Heuristics tend to be unimpressive. They’re often quite simple. In fact, that’s the whole point: heuristics abbreviate. They also “don’t really prove anything.” I’m reminded of the chorus of complaints that greeted the Google ngram viewer when it first came out, to the effect that “no one knows what these graphs are supposed to prove.” Perhaps they don’t prove anything. But I find that in practice they’re already guiding my research, by doing a kind of temporal orienteering for me. I might have guessed that “man of fashion” was a buzzword in the late eighteenth century, but I didn’t know that “diction” and “excite” were as well.

diction, excite, Diction, Excite, in English corpus, 1700-1900

What does a miscellaneous collection of facts about different buzzwords prove? Nothing. But my point is that if you actually want to transform a discipline, sometimes it’s a good idea to prove nothing. Give people a new heuristic, and let them decide what to prove. The discipline will be transformed, and it’s quite possible that no one will even realize how it happened.

POSTSCRIPT, May 1, 2011: For a slightly different perspective on this issue, see Ben Schmidt’s distinction between “assisted reading” and “text mining.” My own thinking about this issue was originally shaped by Schmidt’s observations, but on re-reading his post I realize that we’re putting the emphasis in slightly different places. He suggests that digital tools will be most appealing to humanists if they resemble, or facilitate, familiar kinds of textual encounter. While I don’t disagree, I would like to imagine that humanists will turn out in the end to be a little more flexible: I’m emphasizing the “heuristic” nature of both search engines and the ngram viewer in order to suggest that the key to the success of both lies in the way they empower the user. But — as the word “tricked” in the title is meant to imply — empowerment isn’t the same thing as self-determination. To achieve that, we need to reflect self-consciously on the heuristics we use. Which means that we need to realize we’re already doing text mining, and consider building more appropriate tools.

This post was originally titled “Why Search Was the Killer App in Text-Mining.”


Seriously geeking out.

The pace of posts here has slowed, and it may stay pretty slow until I get some new data-slicing tools set up.

I spent the weekend trying to understand when I might want to use a vector space model to compare documents or terms, and when ordinary Pearson’s correlation would be better. Also, I now understand how Ward’s method of hierarchical agglomerative clustering is different from all the other methods.

I know kung fu.

Aside from the sheer fun of geekery, what I’ve learned is that the digital humanities have become *much* easier to enter than they were in the 90s. I attempted a bit of data-mining in the early 90s, and published an article containing a few graphs in Studies in Romanticism, but didn’t pursue the approach much further because I found it nearly impossible to produce the kind of results I wanted on the necessary scale. (You have to remember that my interests lean toward the large end of the scale continuum in DH.)

I told myself that I would get back in the game when the kinds of collections I needed began to become available, and in the last couple of years it became clear to me that they were, if not available, at least possible to construct. But I actually had no idea how transparent and accessible things have become. So much information is freely available on the web, and with tools like Zotero and SEASR the web is also becoming a medium in which one can do the work itself. Everything’s frickin interoperable. It’s so different from the 90s when you had to build things more or less from scratch yourself.

methodology undigitized humanities

Why everyone should welcome the coming controversy over digital humanities.

Over the next several years, I predict that we’re going to hear a lot of arguments about what the digital humanities can’t do. They can’t help us distinguish insightful and innovative works from merely typical productions of the press. They can’t help us make aesthetic judgments. They can’t help students develop a sense of what really matters about their individual lives, or about history.

Personally, I’m going to be thrilled. First of all, because Blake was right about many things, but above all about the humanities, when he wrote “Opposition is true Friendship.” The best way to get people to pay attention to the humanities is for us to have a big, lively argument about things that matter — indeed, I would go so far as to say that no humanistic project matters much until it gets attacked.

And critics of the digital humanities will be pointing to things that really do matter. We ought to be evaluating authors and works, and challenging students to make similar kinds of judgments. We ought to be insisting that students connect the humanities to their own lives, and develop a broader feeling for the comic and tragic dimensions of human history.

William Blake, "Newton," 1795

Of course, it’s not as though we’re doing much of that now. But if humanists’ resistance to the digitization of our profession causes us to take old bromides about the humanities more seriously, and give them real weight in the way we evaluate our work — then I’m all for it. I’ll sign up, in full seriousness, as a fan of the coming reaction against the digital humanities, which might even turn out to be more important than digital humanism itself.

I wouldn’t, after all, want every humanist to become a “digital humanist.” I believe there’s a lot we can learn from new modes of analysis, networking, and visualization, but I don’t believe the potential is infinite, or that new approaches ineluctably supplant old ones. The New York Times may have described data-intensive methods as an alternative to “theory,” but surely we’ve been trained to recognize a false dichotomy? “Theory” used to think it was an alternative to “humanism,” and that was wrong too.

I also predict that the furor will subside, in a decade or so, when scholars start to understand how new modes of analysis help them do things they presently want to do, but can’t. I’ve been thinking a lot about Benjamin Schmidt’s point that search engines are already a statistically sophisticated technology for assisted reading. Of course humanists use search engines to mine data every day, without needing to define a tf-idf score, and without getting so annoyed that they exclaim “Search engines will never help us properly appreciate an individual author’s sensibility!”

That’s the future I anticipate for the digital humanities. I don’t think we’re going to be making a lot of arguments that explicitly foreground a quantitative methodology. We’ll make a few. But more often text mining, or visualization, will function as heuristics that help us find and recognize significant patterns, which we explore in traditional humanistic ways. Once a heuristic like that is freely available and its uses are widely understood, you don’t need to make a big show of using it, any more than we now make a point of saying “I found these obscure sources by performing a clever keyword search on ECCO.” But it may still be true that the heuristic is permitting us to pursue different kinds of arguments, just as search engines are now probably permitting us to practice a different sort of historicism.

But once this becomes clear, we’ll start to agree with each other. Things will become boring again, and The New York Times will stop paying attention to us. So I plan to enjoy the argument while it lasts.

methodology ngrams

The Google dataset as an episode in the history of science.

In a few years, some enterprising historian of science is going to write a history of the “culturomics” controversy, and it’s going to be fun to read. In some ways, the episode is a classic model of the social processes underlying the production of knowledge. Whenever someone creates a new method or tool (say, an air pump), and claims to produce knowledge with it, they run head-on into the problem that knowledge is social. If the tool is really new, their experience with it is by definition anomalous, and anomalous experiences — no matter how striking — never count as knowledge. They get dismissed as amusing curiosities.

Robert Boyle's air pump.

The team that published in Science has attempted to address this social problem, as scientists usually do, by making their data public and carefully describing the conditions of their experiment. In this case, however, one runs into the special problem that the underlying texts are the private property of Google, and have been released only in a highly compressed form that strips out metadata. As Matt Jockers may have been the first to note, we don’t yet even have a bibliography of the contents of each corpus. Yesterday, in a new FAQ posted on (see section III.5), researchers acknowledged that they want to release such a bibliography, but haven’t yet received permission from Google to do it.

This is going to produce a very interesting deadlock. I’ve argued in many other posts that the Google dataset is invaluable, because its sheer scale allows us to grasp diachronic patterns that wouldn’t otherwise be visible. But without a list of titles, it’s going to be difficult to cite it as evidence. What I suspect may happen is that humanists will start relying on it in private to discover patterns, but then write those patterns up as if they had just been doing, you know, a bit of browsing in 500,000 books — much as we now use search engines quietly and without acknowledgment, although they in fact entail significant methodological choices. As Benjamin Schmidt has recently been arguing, search technology is based on statistical presuppositions more complex and specific than most people realize, presuppositions that humanists already “use all the time to, essentially, do a form of reading for them.”

A different solution, and the one I’ll try, is to use the Google dataset openly, but in conjunction with other smaller and more transparent collections. I’ll use the scope of the Google dataset to sketch broad contours of change, and then switch to a smaller archive in order to reach firmer and more detailed conclusions. But I still hope that Google can somehow be convinced to release a bibliography — at least of the works that are out of copyright — and I would urge humanists to keep lobbying them.

If some of the dilemmas surrounding this tool are classic history-of-science problems, others are specific to a culture clash between the humanities and the sciences. For instance, I’ve argued in the past that humanists need to develop a quantitative conception of error. We’re very talented at making the perfect the enemy of the good, but that simply isn’t how statistical knowledge works. As the newly-released FAQ points out, there’s a comparably high rate of error in fields like genomics.

On other topics, though, it may be necessary for scientists to learn a bit more about the way humanists think. For instance, one of the corpora included in the ngram viewer is labeled “English fiction.” Matt Jockers was the first to point out that this is potentially ambiguous. I assumed that it contained mostly novels and short stories, since that’s how we use the word in the humanities, but prompted by Matt’s skepticism, I wrote the culturomics team to inquire. Yesterday in the FAQ they answered my question, and it turns out that Matt’s skepticism was well founded.

Crucially, it’s not just actual works of fiction! The English fiction corpus contains some fiction and lots of fiction-associated work, like commentary and criticism. We created the fiction corpus as an experiment meant to explore the notion of creating a subject-specific corpus. We don’t actually use it in the main text of our paper because the experiment isn’t very far along. Even so, a thoughtful data analyst can do interesting things with this corpus, for instance by comparing it to the results for English as a whole.

Humanists are going to find that an eye-opening paragraph. This conception of fiction is radically different from the way we usually understand fiction — as a genre. Instead, the culturomics team has constructed a corpus based on fiction as a subject category; or perhaps it would be better to say that they have combined the two conceptions. I can say pretty confidently that no humanist will want to rely on the corpus of “English fiction” to make claims about fiction; it represents something new and anomalous.

On the other hand, I have to say that I’m personally grateful that the culturomics team made this corpus available — not because it tells me much about fiction, but because it tells me something about what happens when you try to hold “subject designations” constant across time instead of allowing the relative proportions of books in different subjects to fluctuate as they actually did in publishing history. I think they’re right that this is a useful point of comparison, although at the moment the corpus is labeled in a potentially misleading way.

In general, though, I’m going to use the main English corpus, which is easier to interpret. The lack of metadata is still a problem here, but this corpus seems to represent university library collections more fully than any other dataset I have access to. While sheer scale is a crude criterion of representativeness, for some questions it’s the useful one.

The long and short of it all is that the next few years are going to be a wild ride. I’m convinced that advances in digital humanities are reaching the point where they’re going to start allowing us to describe some large, fascinating, and until now largely invisible patterns. But at the moment, the biggest dataset — prominent in public imagination, but also genuinely useful — is curated by scientists, and by a private corporation that has not yet released full information about it. The stage is set for a conflict of considerable intensity and complexity.

18c 19c methodology ngrams trend mining

Identifying topics with a specific kind of historical timeliness.

Benjamin Schmidt has been posting some fascinating reflections on different ways of analyzing texts digitally and characterizing the affinities between them.

I’m tempted to briefly comment on a technique of his that I find very promising. This is something that I don’t yet have the tools to put into practice myself, and perhaps I shouldn’t comment until I do. But I’m just finding the technique too intriguing to resist speculating about what might be done with it.

Basically, Schmidt describes a way of mapping the relationships between terms in a particular archive. He starts with a word like “evolution,” identifies texts in his archive that use the word, and then uses tf-idf weighting to identify the other words that, statistically, do most to characterize those texts.

After iterating this process a few times, he has a list of something like 100 terms that are related to “evolution” in the sense that this whole group of terms tends, not just to occur in the same kinds of books, but to be statistically prominent in them. He then uses a range of different clustering algorithms to break this list into subsets. There is, for instance, one group of terms that’s clearly related to social applications of evolution, another that seems to be drawn from anatomy, and so on. Schmidt characterizes this as a process that maps different “discourses.” I’m particularly interested in his decision not to attempt topic modeling in the strict sense, because it echoes my own hesitation about that technique:

In the language of text analysis, of course, I’m drifting towards not discourses, but a simple form of topic modeling. But I’m trying to only submerge myself slowly into that pool, because I don’t know how well fully machine-categorized topics will help researchers who already know their fields. Generally, we’re interested in heavily supervised models on locally chosen groups of texts.

This makes a lot of sense to me. I’m not sure that I would want a tool that performed pure “topic modeling” from the ground up — because in a sense, the better that tool performed, the more it might replicate the implicit processing and clustering of a human reader, and I already have one of those.

Schmidt’s technique is interesting to me because the initial seed word gives it what you might call a bias, as well as a focus. The clusters he produces aren’t necessarily the same clusters that would emerge if you tried to map the latent topics of his whole archive from the ground up. Instead, he’s producing a map of the semantic space surrounding “evolution,” as seen from the perspective of that term. He offers this less as a finished product than as an example of a heuristic that humanists might use for any keyword that interested them, much in the way we’re now accustomed to using simple search strategies. Presumably it would also be possible to move from the semantic clusters he generates to a list of the documents they characterize.

I think this is a great idea, and I would add only that it could be adapted for a number of other purposes. Instead of starting with a particular seed word, you might start with a list of terms that happen to be prominent in a particular period or genre, and then use Schmidt’s technique of clustering based on tf-idf correlations to analyze the list. “Prominence” can be defined in a lot of different ways, but I’m particularly interested in words that display a similar profile of change across time.

diction, elegance, in the English corpus, 1700-1900, plus the capitalized 18c versions

For instance, I think it’s potentially rather illuminating that “diction” and “elegance” change in closely correlated ways in the late eighteenth and early nineteenth century. It’s interesting that they peak at the same time, and I might even be willing to say that the dip they both display, in the radical decade of the 1790s, suggests that they had a similar kind of social significance. But of course there will be dozens of other terms (and perhaps thousands of phrases) that also correlate with this profile of change, and the Google dataset won’t do anything to tell us whether they actually occurred in the same sorts of books. This could be a case of unrelated genres that happened to have emerged at the same time.

But I think a list of chronologically correlated terms could tell you a lot if you then took it to an archive with metadata, where Schmidt’s technique of tf-idf clustering could be used to break the list apart into subsets of terms that actually did occur in the same groups of works. In effect this would be a kind of topic modeling, but it would be topic modeling combined with a filter that selects for a particular kind of historical “topicality” or timeliness. I think this might tell me a lot, for instance, about the social factors shaping the late-eighteenth-century vogue for characterizing writing based on its “diction” — a vogue that, incidentally, has a loose relationship to data mining itself.

I’m not sure whether other humanists would accept this kind of technique as evidence. Schmidt has some shrewd comments on the difference between data mining and assisted reading, and he’s right that humanists are usually going to prefer the latter. Plus, the same “bias” that makes a technique like this useful dispels any illusion that it is a purely objective or self-generating pattern. It’s clearly a tool used to slice an archive from a particular angle, for particular reasons.

But whether I could use it as evidence or not, a technique like this would be heuristically priceless: it would give me a way of identifying topics that peculiarly characterize a period — or perhaps even, as the dip in the 1790s hints, a particular impulse in that period — and I think it would often turn up patterns that are entirely unexpected. It might generate these patterns by looking for correlations between words, but it would then be fairly easy to turn lists of correlated words into lists of works, and investigate those in more traditionally humanistic ways.

For instance, I had no idea that “diction” would correlate with “elegance” until I stumbled on the connection, but having played around with the terms a bit in MONK, I’m already getting a sense that the terms are related not just through literary criticism (as you might expect), but also through historical discourse and (oddly) discourse about the physiology of sensation. I don’t have a tool yet that can really perform Schmidt’s sort of tf-idf clustering, but just to leave you with a sense of the interesting patterns I’m glimpsing, here’s a word cloud I generated in MONK by contrasting eighteenth-century works that contain “elegance” to the larger reference set of all eighteenth-century works. The cloud is based on Dunning’s log likelihood, and limited to adjectives, frankly, just because they’re easier to interpret at first glance.

Dark adjectives are overrepresented in a corpus of 18c works that contain "elegance," light ones underrepresented.

There’s a pretty clear contrast here between aesthetic and moral discourse, which is interesting to begin with. But it’s also a bit interesting that the emphasis on aesthetics extends into physiological terms like “sensorial,” “irritative,” and “numb,” and historical terms like “Greek” and “Latin.” Moreover, many of the same terms reoccur if you pursue the same strategy with “diction.”

Dark adjectives are overrepresented in a corpus of 18c works containing "diction," light ones underrepresented.

A lot of words here are predictably literary, but again you see sensory terms like “numb,” and historical ones like “Greek,” “Latin,” and “historical” itself. Once again, moreover, moral discourse is interestingly underrepresented. This is actually just one piece of the larger pattern you might generate if you pursued Schmidt’s clustering strategy — plus, Dunning’s is not the same thing as tf-idf clustering, and the MONK corpus of 1000 eighteenth-century works is smaller than one would wish — but the patterns I’m glimpsing are interesting enough to suggest to me that this general kind of approach could tell me a lot of things I don’t yet know about a period.

methodology ngrams

How to make the Google dataset work for humanists.

I started blogging about the Google dataset because it revealed stylistic trends so intriguing that I couldn’t wait to write them up. But these reflections are also ending up in a blog because they can’t yet go in an article. The ngram viewer, as fascinating as it is, is not yet very useful as evidence in a humanistic argument.

As I’ve explained at more length elsewhere, the problems that most humanists have initially pointed to don’t seem to me especially troubling. It’s true that the data contains noise — but so does all data. Researchers in other fields don’t wait for noiseless instruments before they draw any conclusions; they assess the signal/noise ratio and try to frame questions that are answerable within those limits.

It’s also true that the history of diction doesn’t provide transparent answers to social and literary questions. This kind of evidence will require context and careful interpretation. In which respect, it resembles every other kind of evidence humanists currently grapple with.

Satanic, Satanic influence, Satanic verses, in English corpus, 1800-2000

The problem that seems more significant to me is one that Matt Jockers has raised. We simply don’t yet know what’s in these corpora. We do know how they were constructed: that’s explained, in a fairly detailed way, in the background material supporting the original article in Science. But we don’t yet have access to a list of titles for each corpus.

Here differences between disciplines become amusing. For a humanist, it’s a little shocking that a journal like Science would publish results without what we would call simply “a bibliography” — a list of the primary texts that provide evidence for the assertion. The list contains millions of titles in this case, and would be heavy in print. But it seems easy enough for Google, or the culturomics research team, to make these lists available on the web. In fact, I assume they’re forthcoming; the datasets themselves aren’t fully uploaded yet, so apparently more information is on the way. I’ve written Google Labs asking whether they plan to release lists of titles, and I’ll update this post when they do.

Until they do, it will be difficult for humanists to use the ngram viewer as scholarly evidence. The background material to the Science article does suggest that these datasets have been constructed thoughtfully, with an awareness of publishing history, and on an impressive scale. But humanists and scientists understand evidence differently. I can’t convince other humanists by telling them “Look, here’s how I did the experiment.” I have to actually show them the stuff I experimented on — that is, a bibliography.

Ideally, one might ask even more from Google. They could make the original texts themselves available (at least those out of copyright), so that we could construct our own archives. With the ability to ask questions about genre and context of occurrence, we could connect quantitative trends to a more conventional kind of literary history. Instead of simply observing that a lot of physical adjectives peak around 1940, The Big Sleepwe could figure out how much of that is due to modernism (“The sunlight was hot and hard”), to Time magazine, or to some other source — and perhaps even figure out why the trend reversed itself.

Google seems unlikely to release all their digitized texts; it may not be in their corporate interest to do so. But fortunately, there are workarounds. HathiTrust, and other online archives, are making large electronic collections freely available, and these will eventually be used to construct more flexible tools. Even now, it’s possible to have the best of both worlds by pairing the scope of Google’s dataset with the analytic flexibility of a tool like MONK (constructed by a team of researchers funded by the Andrew W. Mellon Foundation, including several here at Illinois). When I discover an interesting 18c. or 19c. trend in the ngram viewer, I take it to MONK, which can identify genres, authors, works, or parts of works where a particular pattern of word choice was most prominent.

So, to make the ngram viewer useful, Google needs to release lists of titles, and humanists need to pair the scope of the Google dataset with the analytic power of a tool like MONK, which can ask more precise, and literarily useful, questions on a smaller scale. And then, finally, we have to read some books and say smart things about them. That part hasn’t changed.

But the ngram viewer itself could also be improved. It could, for instance

1) Give researchers the option to get rid of case sensitivity and (at least partly) undo the f/s substitution, which together make it very hard to see any patterns in the 18c.

2) Provide actual numbers as output, not just pretty graphs, so that we can assess correlation and statistical significance.

3) Offer better search strategies. Instead of plugging in words one by one to identify a pattern, I would like to be able to enter a seed word, and ask for a list of words that correlate with it across a given period, sorted by degree of positive (or inverse) correlation.

It would be even more interesting to do the same thing for ngrams. One might want the option to exclude phrases that contain only the original seed word(s) and stop words (“of,” “the,” and so on). But I suspect a tool like this could rapidly produce some extremely interesting results.

fight for existence, fight for life, fight for survival, fight to the death, in English, 1800-2000

4) Offer other ways to mine the list of 2,3,4, and 5-grams, where a lot of conceptually interesting material is hiding. For instance, “what were the most common phrases containing ‘feminine’ between 1950 and 1970?” Or, “which phrases containing ‘male’ increased most in frequency between 1940 and 1960?”

Of course, since the dataset is public, none of these improvements actually have to be made by Google itself.

methodology ngrams

Several varieties of noise, and the theme to Love Story.

I’ve asserted several times that flaws in optical character recognition (OCR) are not a crippling problem for the English part of the Google dataset, after 1820. Readers may wonder where I get that confidence, since it’s easy to generate a graph like this for almost any short, random string of letters:

xec, in the English corpus, 1700-2000

It’s true that the OCR process is imperfect, especially with older typography, and produces some garbage strings of letters. You see a lot of these if you browse Google Books in earlier periods. The researchers who created the ngram viewer did filter out the volumes with the worst OCR. So the quality of OCR here is higher than you’ll see in Google Books at large — but not perfect.

I tried to create “xec” as a nonsense string, but there are surprisingly few strings of complete nonsense. It turns out that “xec” occurs for all kinds of legitimate reasons: it appears in math, as a model number, and as a middle name in India. But the occurrences before 1850 that look like the Chicago skyline are mostly OCR noise. Now, the largest of these is three millionths of a percent (10-6). By contrast, a moderately uncommon word like “apprehend” ranges from a frequency of two thousandths of a percent (10-3) in 1700 to about two ten-thousandths of a percent today (10-4). So we’re looking at a spike that’s about 1% of the minimum frequency of a moderately uncommon word.

In the aggregate, OCR failures like this are going to reduce the frequency of all words in the corpus significantly. So one shouldn’t use the Google dataset to make strong claims about the absolute frequency of any word. But “xec” occurs randomly enough that it’s not going to pose a real problem for relative comparisons between words and periods. Here’s a somewhat more worrying problem:

hirn, in the English corpus, 1700-2000

English unfortunately has a lot of letters that look like little bumps, so “hirn” is a very common OCR error for “him.” Two problems leap out here. First, the scale of the error is larger. At its peak, it’s four ten-thousandths of a percent (10-4), which is comparable to the frequency of an uncommon word. Second, and more importantly, the error is distributed very unequally; it increases as one goes back in time (because print quality is poorer), which might potentially skew the results of a diachronic graph by reducing the frequency of “him” in the early 18c. But as you can see, this doesn’t happen to any significant degree:
hirn, him, in the English corpus, 1700-2000

“Hirn” is a very common error because “him” is a very common word, averaging around a quarter of a percent in 1750. The error in this case is about one thousandth the size of the word itself, which is why “hirn” totally disappears on this graph. So even if we postulate that there are twenty equally common ways of getting “him” wrong in the OCR (which I doubt), this is not going to be a crippling problem. It’s a much less significant obstacle than the random year-to-year variability of sampling in the early eighteenth century, caused by a small dataset, which becomes visible here because I’ve set the smoothing to “0” instead of using my usual setting of “5.”

The take-away here is that one needs to be cautious before 1820 for a number of reasons. Bad OCR is the most visible of those reasons, and the one most likely to scandalize people, but (except for the predictable f/s substitution before 1820), it’s actually not as significant a problem as the small size of the dataset itself. Which is why I think the relatively large size of the Google dataset outweighs its imperfections.

By the way, the mean frequency of all words in the lexicon does decline over time, as the size of the lexicon grows, but that subtle shift is probably not the primary explanation for the downward slope of “him.” “Her” increases in frequency from 1700 to the present; “the” remains largely stable. The expansion of the lexicon, and proliferation of nonfiction genres, does however give us a good reason not to over-read slight declines in frequency. A word doesn’t have to be displaced by anything in particular; it can be displaced by everything in the aggregate.

An even better reason not to over-read changes of 5-10% is just that — frankly — no one is going to care about them. The connection between word frequency and discourse content is still very fuzzy; we’re not in a position to assume that all changes are significant. If the ngram viewer were mostly revealing this sort of subtle variation I might be one of the people who dismiss it as trivial bean-counting. In fact, it’s revealing shifts on a much larger scale, that amount to qualitative change: the space allotted to words for color seems to have grown more than threefold between 1700 and 1940, and possibly more than tenfold in fiction.

This is the fundamental reason why I’m not scandalized by OCR errors. We’re looking at a domain where the minimum threshhold for significance is very high from the start, because humanists basically aren’t yet convinced that changes in frequency matter at all. It’s unlikely that we’re going to spend much time arguing about phenomena subtle enough for OCR errors to make a difference.

This isn’t to deny that one has to be cautious. There are real pitfalls in this tool. In the 18c, its case sensitivity and tendency to substitute f for s become huge problems. It also doesn’t know anything about spelling variants (antient/ancient,changed/changd) or morphology (run/ran). And once in a great while you run into something like this:

romantic, in English Fiction, 1800-2000

“Hmm,” I thought. “That’s odd. One doesn’t normally see straight-sided plateaus outside the 18c, where the sample size is small enough to generate spikes. Let’s have a bit of a closer look and turn off smoothing.”
English Fiction got very romantic indeed in 1972.

Yep, that’s odd. My initial thought was the overwhelming power of the movie Love Story, but that came out 1970, not 1972.

I’m actually not certain what kind of error this is — if it’s an error at all. (Some crazy-looking early 18c spikes in the names of colors turn out to be Isaac Newton’s Opticks.) But this only appears in the fiction corpus and in the general English corpus; it disappears in American English and British English (which were constructed separately and are not simply subsets of English). Perhaps a short-lived series of romance novels with “romantic” in the running header at the top of every page? But I’ve browsed Google Books for 1972 and haven’t found the culprit yet. Maybe this is an ill-advised Easter egg left by someone who got engaged then.

Now, I have to say that I’ve looked at hundreds and hundreds of ngrams, and this is the only case where I’ve stumbled on something flatly inexplicable. Clearly you have to have your wits about you when you’re working with this dataset; it’s still a construction site. It helps to write “case-sensitive” on the back of your hand, to keep smoothing set relatively low, to check different corpora against each other, to browse examples — and it’s wise to cross-check the whole Google dataset against another archive where possible. But this is the sort of routine skepticism we should always be applying to scholarly hypotheses, whether they’re based on three texts or on three million.