research notes

Suggested Ways of Citing Digitized Early Modern Texts

On 1 January 2015, 25,000 hand-keyed Early Modern texts entered the public domain and were publicly posted on the EEBO-TCP project’s GitHub page, with an additional 28,000 or so forthcoming into the public domain in 2020.  This project is, to say the least, a massive undertaking and marks a massive sea change in scholarly study of the Early Modern period. Moreover, we nearly worked out how to cite the EEBO texts (the images of the books themselves) just before this happened: Sam Kaislaniemi has an excellent blogpost on how one should cite books in the EEBO Interface (May, 2014), but his main point is replicated here:

When it comes to digitized sources, many if not most of us probably instinctively cite the original source, rather than the digitized version. This makes sense – the digital version is a surrogate of the original work that we are really consulting. However, digitizing requires a lot of effort and investment, so when you view a book on EEBO but only cite the original work, you are not giving credit where it is due. After all, consider how easy it now is to access thousands of books held in distant repositories, simply by navigating to a website (although only if your institution has paid for access). This kind of facilitation of research should not be taken for granted.

In other words, when you use digitized sources, you should cite them as digitized sources. I do see lots of discussions about how to best access and distribute (linked) open data, but these discussion tend to avoid the question of citation. In my perfect dream world every digital repository would include a suggested citation in their README files and on their website, but alas we do not live in my perfect dream world.

For reasons which seem to be related to the increasingly widespread use of the CC-BY licences, which allow individuals to use, reuse, and “remix” various collections of texts, citation can be a complicated aspect of digital collections, although it doesn’t have to be. For example, this site has a creative commons license, but we have collectively agreed that blog posts etc are due citation; the MLA and APA offer guidelines on how to cite blog posts (and tweets, for that matter). If you use Zotero, for example, you can easily scrape the necessary metadata for citing this blog post in up to 7,819 styles (at the time of writing). This is great, except when you want to give credit where credit is due for digitized text collections, which are less easy to pull into Zotero or other citation managers. And without including this information somewhere in the corpus or documentation, it’s increasingly difficult to properly cite the various digitized sources we often use. As Sam says so eloquently, it is our duty as scholars to do so.

Corpus repositories such as CoRD include documentation such as compiler, collaborators, associated institutions, wordcounts, text counts, and often include a recommended citation, which I would strongly encourage as a best practice to be widely adopted.

Screen Shot 2015-08-05 at 11.15.04

Here is a working list of best citation practices outlined for several corpora I am using or have encountered. These have been cobbled together from normative citation practices with input from the collection creators. (Nb. collection creators: please contact me with suggestions to improve these citations).

This is a work in progress, and I will be updating it occasionally where appropriate. Citations below follow MLA style, but should be adaptable into the citation model of choice.

Folger Shakespeare Library. Shakespeare’s Plays from Folger Digital Texts. Ed. Barbara Mowat, Paul Werstine, Michael Poston, and Rebecca Niles. Folger Shakespeare Library, dd mm yyyy.

Mueller, M. “Wordhoard Shakespeare”. Northwestern University, 2004- 2013. Available online:

Mueller, M. “Standardized Spelling WordHoard Early Modern Drama corpus, 1514- 1662”. Northwestern University, 2010. Available online:

Mueller, M. “Shakespeare His Contemporaries: a corpus of Early Modern Drama 1550-1650”. Northwestern University, 2015. Available online:

EEBO-TCP access points:
There are several access points to the EEBOTCP texts, and one problem is that the text IDs included don’t always correspond to the same texts in all EEBO viewers as Paul Schnaffer describes below.

Benjamin Armintor has been exploring the implications of this on his blog, but in general if you’re using the full-text TCP files, you should be citing which TCP database you are using to access the full-text files. Where appropriate, I’ve included a sample citation as well.

1. For texts from, follow the below formula:EEBOTCP michgan

Author. Title. place: year, Early English Books Online Text Creation Partnership, Phase 1, Oxford, Oxfordshire and Ann Arbor, Michigan, 2015. date accessed: dd mm yyyy

Webster, John. The tragedy of the Dutchesse of Malfy As it was presented priuatly, at the Black-Friers; and publiquely at the Globe, by the Kings Maiesties Seruants. The perfect and exact coppy, with diuerse things printed, that the length of the play would not beare in the presentment. London: 1623, Early English Books Online Text Creation Partnership, Phase 1, Oxford, Oxfordshire and Ann Arbor, Michigan, 2015. Available online:, accessed 5 August 2015.

2. For the Oxford Text Creation Partnership Repository ( and the searchable database thereOxford TCP search page

Author. Title. Early English Books Online Text Creation Partnership, phase I: Oxford, Oxfordshire and Ann Arbor, Michigan, 2015 [place: year]. Available online at; Source available at

Rowley, William. A Tragedy called All’s Lost By Lust. Early English Books Online Text Creation Partnership, phase I: Oxford, Oxfordshire and Ann Arbor, Michigan, 2015 [London: 1633]. Available online:; Source available at:

3. The entire EEBO-TCP Github repositoryGithub EEBOTCP

Early English Books Online Text Creation Partnership, Phase I. Early English Books Online Text Creation Partnership, phase I: Oxford, Oxfordshire and Ann Arbor, Michigan, 2015. Available online:

If you are citing bits of the TCP texts as part of the whole corpus of EEBO-TCP, it makes the most sense to parenthetically cite the TCP ID as its identifying characteristic (following corpus linguistic models). So for example, citing a passage from Dutchess of Malfi above would include a parenthetical including the unique TCPID  (A14872).

(Presumably other Text Creation Partnership collections, such as ECCO and EVANS, should be cited in the same manner.)

A cautionary concordance plot tale

In my previous post I addressed how to produce a view of many concordance plots at once, and presented concordance plots for twelve vocatives which are indicative of social class in Shakespeare and a larger reference corpus of Early Modern Drama.

After double-checking all the concordance plot files using a hand-numbered master sheet, I normalised the files using the command convert plot*.jpg -size 415x47! plot*.jpg (on the off chance that any files weren’t ultimately the same size), created a new folder of the normalised files, and pulled out the examples which matched the numbers I had for Shakespeare’s plays for further analysis. I hadn’t addressed titles, as I wasn’t really aiming to look at individual authors, so each file is named plot1, plot2, plot234, etc. I went on to compile the results for these plays, felt confident about the fact that I had isolated Shakespeare, and wrote up my previous blog post.

This morning I had a nagging thought: What if those weren’t Shakespeare’s plays? After all, I had broken my #1 rule about using computational methods – assuming that everything at every step of the process worked the way I thought it did. I am probably a self-parodying pendant when it comes to computational methods, because when something goes wrong at some stage in the coding process it may *never* be visible or even noticed in the final output, and this gives me reason to seriously distrust automated processes for analysis.

Ultimately, I decided I would double-check the plays I had deemed to be “Shakespeare”’s. Even though I hadn’t done much automated processing with the image files, I had assumed that the normalisation process would only change the file names to represent a modified version: so that plot10 would become plot0-10, plot 11 would become plot0-11, plot234 would become plot0-234. I had assumed the information in these files wouldn’t change, and the names would correspond to the original files.

This was not true. Instead, I had isolated a very nice sample of 36 plays which I thought matched Shakespeare’s plays in numbering, but turned out to be sampled from throughout the corpus. Matching the sampled “Shakespeare” concordance plots to the master document of concordance plots, I found that I had at least one Middleton play and at least one Seneca play in addition to some (but not all) Shakespeare plays.[1] At this point I was worried, so I re-created Shakespeare’s concordance plots from the master document of concordance plots. By redoing the concordance plots, I could guarantee that these were at least all Shakespeare’s plays in the first instance. Then I normalised them again for size, and went back to see what happens in that process. The first files were a perfect match, as I had hoped. But once I moved to the second concordance plot, I was in trouble.

Below is an image showing the unmodified concordance plot for The Taming of the Shrew (shx2), outlined in red and on the top left-hand side.The other eight concordance plots in this image are normalised for size, and even without great detail you can tell that none of these match the original file. You don’t even need to see the whole image to see this:

Screen Shot 2015-02-26 at 3.26.03

In other words, as I had suspected, the names of the normalised files didn’t correspond to the original file names, though they were all there.[2] More worryingly, I hadn’t caught it because I had assumed that the files were fine after running a process on them. The files produced results, and if I hadn’t double-checked (really, at this point, triple-checked), I wouldn’t have caught this discrepancy.

So what do concordance plots for Shakespeare’s plays look like in composite for the vocatives attached to a name in a bigram (reminder of search terms: lord [A-Z]|sir [A-Z]|master [A-Z]|duke [A-Z]|earl [A-Z]|king [A-Z]|signior [A-Z]|lady [A-Z]|mistress [A-Z]|madam [A-Z]|queen [A-Z]|dame [A-Z]) look like? Well, surprisingly, not so different from the sample curated previously, which may be less indicative of a specific authorial style:


Remember, we read these from left to right; now there’s a lot of use of vocatives in the very beginning of the plays, which stay quite strong near the rising action until there’s a relative absence just before and around the climax and the start of the falling action. Curiously, the heavy double hit || towards the end is still very visible, as well as a few more dark lines leading up to the conclusion. In some ways, the absence of these vocatives is almost more consistent, and therefore the white bits are more visible.

In the meantime I’m having a fascinating discussion with Lauren Ackerman about how to best address pixel density and depth of detail (especially in the larger EM play corpus), so maybe there will be a third instalment of concordance plots in the future.

[1] Seneca’s plays were published in the 1550s and 1560s, which is why they are included in this data set of printed plays in Early Modern London.

[2] The benefits of working with a smaller set like this means that there are are much smaller, finite number of texts to address: rather than n = 332332 possible combinations, I was now only looking at a possibility of n = 3636. So that was an improvement. In case you’re wondering what happened to one play, because previously I had claimed there were 37 Shakespeare plays, one play doesn’t have any instances of the vocatives being addressed in a bigram with a capital letter.

How to address many concordance plots at once

What if you could take many concordance plots and layer them to get a composite view of many concordance plots in one image? I wanted to see if vocatives which mark for high-status individuals attached to a name appear in any particular pattern which resembles Freytag’s model of dramatic structure.[1]

I selected 12 vocatives which clearly illustrate social class attached to a word beginning with a capital letter for analysis, all of which are relatively frequent in the corpus of 332 plays comprising of 7,305,366 words. In order to get my concordance plots for vocatives attached to a name, I used regular expressions searching for the vocative in question in a bigram with a capital letter strung together by pipelines, so the resulting search looked like this (signior is spelled incorrectly; this is the spelling which produced hits – I suspect something happened in the spelling normalisation stage):
lord [A-Z]|sir [A-Z]|master [A-Z]|duke [A-Z]|earl [A-Z]|king [A-Z]|signior [A-Z]|lady [A-Z]|mistress [A-Z]|madam [A-Z]|queen [A-Z]|dame [A-Z]

Although the regular expression I used picked up examples of queen I and the like, the examples of a capital letter representing the start of a name was far more frequent overall. In the case of mistress, Alison Findlay’s definition (“usually a first name or surname, is a form of polite address to a married woman, or an unmarried woman or girl” (2010, 271) ) accounts for its inclusion here. Though there are certainly complicated readings of this title, I consider instances of mistress to be at the very least a vocative relating to social class in Early Modern England.

The obvious solution to doing this kind of work is R, as people such as Douglas Duhaime and Ted Underwood have been making some gorgeous composite graphs with R for a number of years. To be honest, I didn’t really want to go through the process of addressing a corpus by writing an entire script to produce something that I know can be done quickly and easily in AntConc‘s concordance plot view: I had one specific need; AntConc is an existing framework for producing concordance plots which are normalised for length, as well as a KWIC viewer and several other statistical analyses. I knew that if i wanted to check anything, I could do it easily. I didn’t feel any real need to reinvent the wheel by scripting to accomplish my task, unlike the general DIY process presented by R or Python.[2] The only real downside is that if you want to do more with the output, you have to move into another software package to do that, but even that is not the end of the world.

Ultimately, what I wanted to do was take concordance plots for 332 plays and layer them for a composite picture of how they appear, rather than address them as individual views on a play-by-play basis. Layering images is a common way of addressing edits in printed books; Chris Forster has done exactly that with magazine page size; he suggested I use ImageMagick, a command line processing tool for image compositioning.[3] I have a similarly normalised view of texts at my disposal, as each concordance line is normalized for length. Moreover, Chris and I are of the same mind when it comes to not introducing more complicated software for the sake of using software, so when he told me about this I was willing to give it a try, especially as he has successfully done exactly what I was trying to do. But first I needed concordance plots.

AntConc produces concordance plots but won’t export them, which is annoying but not as annoying as you may think. 38 screen grabs later, I had .pngs of each play’s concordance line. Here they are in AntConc:

womp womp(If you’re not used to reading concordance lines, you read them from left to right (from “start” to “finish”, in narrative terms); each | = 1 hit; the more hits closer together, the darker the line will look.)

I turned these screenshots into a very large jpg with the help of an open source image editing program, just to have them all in one document together. The most well-known is probably GIMP but both lifehacker and Oliver Mason offer Seashore as a more mac-friendly alternative to the GIMP.[4]
Then I broke the master document into individual concordance plots, sized 415×47, using Seashore’s really good select-copy-make new document from pasteboard option, which let you keep and move the select box around the master document, as seen below. Screen Shot 2015-02-23 at 12.15.10So far I have only used regular expressions, command-shift-4, copy, paste, save as .jpg, and pen & paper to record what I was doing. Nothing complicated! It took a while, but in the process I got to know these results really well. Not all the plays in the corpus contain all, or in fact, any, of each vocative: in some instances, there are plays that didn’t use any of the above titles, and aren’t included in this output; some plays only use one vocative out of the twelve investigated or any combination of vocatives which do not represent the full twelve.

As a test, I separated out Shakespeare’s plays to see what a bunch of concordance plots looked like in composite. To do this, I opened a terminal, moved to the correct directory, which comprised moving through 6 directories. Then I normalised everything to the same size with
convert plot*.jpg -size 415x47! plot*.jpg, just in case.
I put those in a new folder of normalised images.
Then, from the directory of normalised images: convert plot*.jpg -evaluate-sequence mean average_page.jpg.

Here’s what 12 vocatives for social class in 37 Shakespeare plays look like in composite:
average shx play_monochrome
There are a few things that I notice in this plot: There’s a quick use of naming vocatives near the beginning of the plays, a relative absense immediately after, but during the rising action and climax there are clear sections which use these vocative quite heavily- especially in the build-up to the climax. Usage drops in the falling action, until just before the denoument; there is a point where vocatives are used quite consistently heavily, marked by || but surrounded by white on both sides. If you can’t see it, here is the concordance plot again, with that point highlighted in red.

If you repeat the above process for the 332 plays, you get the following composite image. Although the amount of information in some ways obfuscates what you’re trying to see, there are darker and lighter bits to this image.
avg EM drama play_monochrome
Most notably, the rising action has a similar cluster of class-status vocative use at the tail end of the introduction and into the rising action, a relative absence until the climax, and then the use of vocatives for social class seem to pick up towards the falling action and end of the plays. Interestingly, the same kind of || notation is visibible towards the conclusion, though it reduplicates itself twice. (Again, if you can’t see it, I’ve highlighted it in red here).

Now to address the details of these plots… But you should also read the follow-up post about this, as well.

tl;dr version:

Can you look at many concordance plots at the same time? Yes.

Do vocatives attached to a name which mark for class status have recognizable patterns in dramatic structure? MAYBE.

[1] Matthew Jockers (1, 2) and Benjamin Schmidt have been doing interesting things with regards to computationally analyzing dramatic structure. I’m not going anywhere near their levels of engagement with dramatic arcs in this post, but they are interesting reads nonetheless. (Followup: Annie Swafford’s blog post on Jockers’ analyses are worth a read as well)

[2] If you particularly enjoy using R to achieve relatively simple tasks like concordance plots, Stefan Gries’ 2009 cookbook Quantitative corpus linguistics with R: a practical introduction and Matthew Jockers’ 2014 cookbook Text Analysis with R for Students of Literature both outline how to do this.

[3] Okay, so this required a few more steps of code, most of which were install scripts which require very little work on the human end beyond following directions of ‘type this, wait for computer to return the input command’. If you are on a mac, you will need to get Xcode to download macports to download ImageMagick, and then X11 to display output. X11 seems optional, especially if you keep your finder window open nearby. Setting all this up took about two hours.

[4] It transpired that I could have done this with ImageMagick using the command
convert -append plots*.png out.png.
Oh well. Seashore also offers layering capabilities for the more graphic design driven amongst you but perhaps more importantly for me, it looks a lot like my dearly beloved MS Paint, a piece of software I’ve been trying to find a suitable replacement for since I joined The Cult of Mac in 2006.

Of time, of numbers and due course of things

[This is the text, more or less, of a paper I presented to the audience of the Scottish Digital Humanities Network’s “Getting Started In Digital Humanities” meeting in Edinburgh on 9 June 2014. You can view my slides here (pdf)]

Computers help me ask questions in ways that are much more difficult to achieve as a reader. This may sound obvious: reading a full corpus of plays, or really any text, takes time, and by the time I closely read all of them, I will have either have not noticed the minutae of all the texts or I will not have remembered some of them. Here, for example, is J. O. Halliwell-Phillipp’s The Works of William Shakespeare; the Text Formed from a New Collation of the Early Editions: to which are Added, All the Original Novels and Tales, on which the Plays are Founded; Copious Archæological Annotations on Each Play; an Essay on the Formation of the Text: and a Life of the Poet, which takes up quite a bit of space on a shelf:IMG_20140604_160953

This isn’t a criticism, nor is it an excuse for not reading; it just means that humans are not designed to remember the minutae of collections of words. We remember the thematic aboutness of them, but perhaps not always the smaller details. Having closely read all these plays (though not in this particular edition: I have read the Arden editions, which were much more difficult to stick on one imposing looking shelf), all I remember what they were about, but perhaps not at the level of minutae I might want to have. So today I’m going to illustrate how I might go from sixteen volumes of Shakespeare to a highly specific research question, and to do that, I’m going to start with a calculator.

A calculator is admittedly a rather old and rather simple piece of technology; it’s one that is not particularly impressive now that we have cluster servers that can crunch thousands of data points for us, but it remains useful nonetheless. Without using technology which is more advanced than our humble calculator, I’m going to show how the simple task of counting and a little bit of basic arithmetic can raise some really interesting questions. Straightforward counting is starting to get a bit of a bad rap in digital humanities discourse (cf Jockers and Mimno 2013, 3 and Goldstone and Underwood 2014, 3-4): yes, we can count, but that is simple. We can also complicate this process with calculation and get even more exciting results! This is, of course, true, and provides many new insights to texts which were otherwise unobtainable. Eventually today I will get to more advanced calculation, but for now, let’s stay simple and count some things.

Except that counting is not actually all that simple: decisions have to be made about what to count and how to decide what to count, and then how you are going to do that. I happen to be interested in gender, which I think is one of the more quantifiable social identity variables in textual objects, though it certainly isn’t the only one. Let’s say I wanted to find three historically relevant gendered noun binaries for Shakespeare’s corpus. Looking at the historical thesaurus of the OED for historical contexts, I can decide on lord/lady, man/woman, and knave/wench, as they show a range of formalities (higher – neutral – lower) and these terms are arguably semantically equivalent. The first question I would have is “how often do these terms actually appear in 38 Shakespeare plays?”

shx minus node words pie chart

Turns out the answer is “not much”: they are right up there in the little red sliver there. My immediate next question would be “what makes up the rest of this chart?” The obvious answer is, of course, that it covers everything that is not our node words in Shakespeare. However, there are two main categories of words contained therein: the frequency of function words (those tiny boring words that make up much of language) and the frequency of content words (words that make up what each play is about). We have answers, but instantly I have another question: what does the breakdown of that little red sliver look like?

This next chart shows the frequency of both the singular and the plural form of each node word, in total, for all 38 Shakespeare plays. There are two instantly noticeable things in this chart: first, the male terms are far more frequent than the female terms, and that wench is not used very much (though we may think of wench as being a rather historical term).
individual node word plurals in Shakespeare (full)

There are more male characters than female characters in Shakespeare – by quite a large margin, regardless of how you choose to divide up gender – but surely they are talking about female characters (as they are the driving force of these plays: either a male character wants to marry or kill a female character). This is not to say that male and female characters won’t talk to each other; there just happens to be a lot more male characters. Biber and Burges (2000) have noted that in 19th century plays, male to male talk is more frequent than male to female talk (and female to female talk). I am not going to claim this is true here, but it seems to be a suggestive model, as male characters dominate speech quantities in the plays. There are lots of questions we can keep asking from this point, and I will return to some of them later, but I want to ask a bigger question: how does Shakespeare’s use of these binaries compare to a larger corpus of his contemporaries, 1512-1662?

It is worth noting that this corpus contains 332 plays, even though it is called the 400 play corpus; some things, I suppose, sound better when rounded up. These terms are still countable, though, and we see a rather different graph for this corpus:
400 play corpus full node words frequencies

The 400 play corpus includes Shakespeare, so we are now comparing Shakespeare to himself and 54 other dramatists.[1] The male nouns are noticeably more frequent than the female nouns, which suggests that maybe the proportions of male to female characters from Shakespeare is true here too. Interestingly, lord is less frequent than man, which is the opposite of what we saw previously. The y axis is different for this graph, as this is a much larger corpus than Shakespeare’s, but it seems like the female nouns are consistent.

One glaring problem with this comparison is that I am looking at two different-sized objects. A corpus of 332 plays is going to be, generally speaking, larger than a corpus of 38 plays.[2] McEnery and Wilson note that comparisons of corpora often require adjustment: “it is necessary in those cases to normalize the data to some proportion […] Proportional statistics are a better approach to presenting frequencies” (2003, 83). When creating proportions, Adam Kilgariff notes “the thousands or millions cancel out when we do the division, it makes no difference whether we use thousands or millions” (2009, 1), which follows McEnery and Wilson’s assertion that “it is not crucial which option is selected” (2003, 84). For my proportions, I choose parts per million.
Shakespeare from Martin's corpus 12.16.10 and Martin's Corpus, normalized plural node words graphed
Shakespeare is rather massively overusing lord in his plays compared to his contemporaries, but he is also underusing the female nouns compared to contemporaries. Now we have a few research questions to address, all of which are very interesting:

  • Why does Shakespeare use lord so much more than the rest of Early Modern dramatists?
  • Why do the rest of Early Modern dramatists use wench so much more than Shakespeare?
  • Why is lady more frequent than woman overall in both corpora?

I’m not going to be able to answer all of these today, though they but let’s talk a little bit about lord. This is a pretty noticeable difference for a term which seems pretty typical of Early Modern drama, which is full of noblemen. If I had to guess, I would say that lord might be more frequent in history plays compared to the tragedies or the comedies. I say this because as a reader I know there are most definitely noblemen, and probably defined as such, in these plays.

So what if we remove the histories from Shakespeare’s corpus, count everything up again, and make a new graph comparing Shakespeare minus the histories to all of Shakespeare? By removing the history plays it is possible to see how Shakespeare’s history plays as a unit compare to his comedy & tragedy plays as a unit. [3]
Shakespeare minus histories compared to shakespeare with histories per million
Female nouns fare better in Shakespeare Without Histories than in Shakespeare Overall, possibly because the female characters are more directly involved in the action of tragedies and comedies than they are in histories (though we know the Henry 4 plays are an exception to that), so that is perhaps not all that interesting. What is interesting, though, is the difference between lord in Shakespeare Without Histories and Shakespeare With Histories. What is going on in the histories? How do Shakespeare’s histories compare to all histories in the 400 play corpus?
history plays, shx vs history plays from 400 play corpus
Now we have even more questions, especially “what on earth is going on with lord in Shakespeare” and “why is wench more frequent in all of the histories?” I’m going to leave the wench question for now, though: not because it’s uninteresting but because it is less noticeable compared to what I’ve been motioning at with lord, which is clearly showing some kind of generic variation.

Remember, we haven’t done anything more complex than counting and a little bit of arithmetic yet, and we have already created a number of questions to address. Now we can create an admittedly low-tech visualization of where in the history plays these terms show up: each black line is one instance, and you read these from left to right (‘start’ to ‘finish’):
Screen shot 2014-06-06 at 4.30.36
And now I instantly have more questions (why are there entire sections of plays without lord? Why do they cluster only in what clearly are certain scenes? etc) but what looks most interesting to me is King John, which has the fewest examples. On a first glance, King John and Richard 3 appear to be outliers (that is, very noticeably different from the others: 42 instances vs 236 instances). Having read King John, I know that there are definitely nobles in the play: King John, King Philip, the Earls of Sudbury, Pembroke, Essex and the excellently named Lord Bigot. And, again, having read the play I know that it is about the relationships between fathers, mothers and brothers – the play centers around Philip the Bastard’s claim to the throne – and also is about the political relationship (or lack thereof) between France and England. From a reader’s perspective, none of that is particularly thematically unique to this play compared to the rest Shakespeare’s history plays, though.

I can now test my reader’s perspective using a statistical measure of keyness called log likelihood, which asks which words are more or less likely to appear in an analysis text compared to a larger corpus. This process will provide us with words which are positively and negatively ranked overall with a ranking of statistical significance (more stars means more statistically significant). Now I am asking the computer to compare King John to all of Shakespeare’s histories. I have excluded names from this analysis, as a reader definitely knows hubert arthur robert philip faulconbridge geoffrey are in this play without the help of the computer.
Screen shot 2014-06-03 at 10.20.23
However, you can see that the absence of lord in King John is highly statistically significant (marked with four *s, compared to others with fewer *s). Now, we saw this already with the line plots, though it is nice to know that this is in fact one of the most significant differences between King John and the rest of the histories.

All of this is nice, and very interesting, as it is something we might not have ever noticed as a reader: because it is a history play with lords in it, it is rather safe to assume that it will contain the word lord more often than it actually does. Revisiting E.A.J. Honingmann’s notes on his Arden edition of King John, there have been contentions about the use of king in the First Folio (2007, xxxiii-xliii), most notably around the confusions surrounding King Lewis, King Philip and King John all labeled as ‘king’ in the Folio (see xxxiv-xxxvii for evidence). But none of this is answering our question about lord’s absence. So what is going on with lord? We can identify patterns with a concordancer, and we get a number of my lords:Screen shot 2014-06-03 at 10.37.59
This is looking like a fairly frequent construction: we might want to see what other words are likely to appear near lord in Shakespeare overall: is my one of them? As readers, we might not notice how often these two words appear together. I should stress that we still have not answered our initial question about lord in King John, though we are trying to.

Using a conditional probability of the likelihood of one lemma (word) to appear next to another lemma (word) in a corpus using the dice coefficiency test, which is the mean of two conditional probabilities: P(w1,w2) and P(w2,w1). Assuming the 2nd word in the bigram appears given the 1st word, and the 1st word in the bigram appears given the 2nd word, this relationship can be computed on a scale from 0-1. 0 would mean there is no relationship; 1 means they always appear together. With this information, you can then show which words are uniquely likely to appear near lord in Shakespeare and contrast that to the kinds of words which are uniquely likely to appear next to lady – and again for the other binaries as well. Interestingly, my only shows up with lord!

Screen shot 2014-06-03 at 10.49.51

This is good, because it shows that lord does indeed appear very differently to our other node words in Shakespeare’s corpus, and suggests that there’s something highly specific going on here with lord, all of which is still suggestive that there is something about lord which is notable. However, I’m still not sure what is happening with lord in King John. Why are there so few instances of it?

Presumably if there is an absence of one word or concept, there will be more of a presence a second word or concept. One such example might be king, but the log-likelihood analysis shows that this is comparatively more frequent in King John than in the rest of Shakespeare’s histories (note the second entry on this list)
Screen shot 2014-06-03 at 10.20.23

Now we have two questions: why is lord so absent, and why is this so present? From here I might go back to our concordance plot visualizations, but this is addressable at the level of grammar: this is a demonstrative pronoun, which Jonathan Hope defines in Shakespeare’s Grammar as “distinguish[ing] number (this/these) and distance (this/these = close; that/those = distant). Distance may be spatial or temporal (for example ‘these days’ and ‘those days’)” (Hope 2003, 24). Now we have a much more nuanced question to address, which a reader would never have noticed: Does King John use abstract, demonstrative pronouns to make up for a lack of the concrete content word lord in the play? I admit I have no idea: does anybody else know?


Halliwell-Phillipps, J.O. (1970. [1854].) The works of William Shakespeare, the text formed from a new collation of the early editions: to which are added all the original novels and tales on which the plays are founded; copious archæological annotations on each play; an essay;on the formation of the text; and a life of the poet. New York: AMS press.

“Early English Books Online: Text Creation Partnership”. Available online: and

“Early English Books Online: Text Creation Partnership”. Text Creation Partnership. Available online:

Anthony, L. (2012). AntConc (3.3.5m) [Computer Software]. Tokyo, Japan: Waseda University. Available from

Biber , Douglas, and Jená Burges. (2000) “Historical Change in the Language Use of Women and Men: Gender Differences in Dramatic Dialogue”. Journal of English Linguistics 28 (1): 21-37.

DEEP: Database of Early English Playbooks. Ed. Alan B. Farmer and Zachary Lesser. Created 2007. Accessed 4 June 2014. Available online:

Froehlich, Heather. (2013) “How many female characters are there in Shakespeare?” Heather Froehlich. 8 February 2013.

Froehlich, Heather. (2013). “How much do female characters in Shakespeare actually say?” Heather Froehlich. 19 February 2013.

Froehlich, Heather. (2013). “The 400 play corpus (1512-1662)”. Available online: [.csv file]

Goldstone, Andrew, and Ted Underwood. “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us.” New Literary History, forthcoming.

Hope, Jonathan. (2003). Shakespeare’s Grammar. The Arden Shakespeare. London: Thompson Learning.

Jockers, M.L. and Mimno, D. (2013). Significant themes in 19th-century literature. Poetics.

Kay, Christian, Jane Roberts, Michael Samuels, and Irené Wotherspoon (eds.). (2014) The Historical Thesaurus of English. Glasgow: University of Glasgow.

Kilgariff, Adam. (2009). “Simple Maths for Keywords”. Proceedings of the Corpus Linguistics Conference 2009, University of Liverpool. Ed. Michaela Mahlberg, Victorina González Díaz, and Catherine Smith. Article 171. Available online:

McEnery, Tony and Wilson, Andrew. (2003). Corpus Linguistics: An Introduction. Edinburgh: Edinburgh University Press, 2nd Edition. 81-83

Mueller, Martin. WordHoard. [Computer Software]. Evanston, Illinois: Northwestern University.

Shakespeare, William. (2007). King John. Ed. E. A. J. Honigmann. London: Arden Shakespeare / Cengage Learning.

[1] Please see [.csv file] for the details of contents in the corpus.

[2] This is not always necessarily true: counting texts does not say anything about how big the corpus is! A lot of very short texts may actually be the same size as a very small corpus containing a few very long texts.

[3] The generic decisions described in this essay have been lifted from DEEP and applied by Martin Mueller at Northwestern University. I am very slowly compiling an update to these generic distinctions from DEEP, which uses Annals of English Drama, 975-1700, 3rd edition, ed. Alfred Harbage, Samuel Schoenbaum, and Sylvia Stoler Wagonheim (London: Routledge, 1989) as its source to Martin Wiggins’ more recent British Drama: A Catalog, volumes 1-3 (Oxford: Oxford UP, 2013a, 2013b, 2013c) for further comparison.

Early “English” Books Online?

Early English Books Online, or EEBO, is what might be technically known as “a hot mess”. (If you’re unfamiliar with EEBO and its messiness, I highly recommend Ian Gadd’s “The Use and Misuse of Early English Books Online” which summarizes how we arrived at this hot mess, Sarah Werner’s blogpost on the kinds of things EEBO doesn’t show us well, and Daniel Powell’s roundup of EEBO weirdness). I want to stress that this isn’t necessarily a bad thing, as it’s a product of time and technology from a while ago.  It’s being rekeyed by humans (the TCP enterprise), and overall it is just a really big dataset of Early Modern English. When you’re looking at giant datasets like EEBO it doesn’t really matter if parts of it are imperfect. It will always be imperfect.

I’ve been looking at spelling variation for various gender terms and collocational patterns surrounding gender terms in EEBO lately  because it is a really big dataset and those tend to be useful for testing our perceptions of language, especially when they contain a number of different kinds of texts. One of the ones I was looking at was hir, a known variable spelling of her. One example of this is can be found in Shakespeare’s Merry Wives of Windsor (V.ii.2150);  Melchiori’s Arden Shakespeare edition has a note about the phrase “his muffler”: the Folio edition of Wives reads his, but the Quarto edition read “her muffler”. This “may be Evans’ confusion, but more likely Shakespeare’s slip or a printer’s misreading of ‘hir’, an alternative spelling of her” (2000: 253).

So in looking for examples of hir I found myself suddenly looking at Welsh. Specifically, this text, Ymadroddion bucheddol ynghylch marvvolaeth o waith Dr. Sherlock (all links will go to the Michigan Text Creation Partnership permalinks, for ease of reference. Because I’m based the UK, my access comes from JISC Historic Ebooks, not the Chadwyck interface, meaning that generated permalinks might not work – further problems!). The below image is from the ‘text’ option on the JISC interface for Ymadroddion:

Screen shot 2013-08-27 at 12.46.01I don’t speak – or read – Welsh, let alone Early Modern Welsh, so I turned first to google translate and secondly to twitter, where I joyfully found a number of people who either work with or speak/read Welsh (and one person who studied Medieval Welsh in undergrad – officially winning the title of ‘most obscure gen ed ever’. The internet continues to amaze.)

In welsh, hir means ‘long’, so it’s not a pronoun but an adjective. I was curious about the structures of grammatical gender in Welsh, namely if it would have agreement by gender in ways that Old English, for example, did. This answer was a little bit more complicated to elucidate but it was declared that yes, there is a gender system in Welsh; and no, it should not affect hir. [1] So, that’s good to know. But here’s a question: When we say ‘Early English Books (Online)’, do we really mean English the place, or English the language?

Linguistically, Welsh is rather decidedly not English, as the extremely useful BBC Modern Welsh Grammar  will illustrate. But I was rather surprised to find Welsh being considered part of “English” in this set. So, I went back to the EEBO-TCP site , where they say the following about text selection:

  • Selection is based on the New Cambridge Bibliography of English Literature (NCBEL). Works are eligible to be encoded if the name of their author appears in NCBEL. Anonymous works may also be selected if their titles appear in the bibliography. The NCBEL was chosen as a guideline because it includes foundational works as well as less canonical titles related to a wide variety of fields, not just literary studies.

  • In general, we prioritize selection of first editions and works in English (although in the past we have also tackled Latin and Welsh texts). Because our funding is limited, we aim to key as many different works as possible, in the language in which our staff has the most expertise. However, exceptions for specific works may be made upon request.

  •  A work will not be passed over for encoding simply because it is available in another electronic collection. Not only is the quality of these collections sometimes uncertain, a text’s presence outside of EEBO will not allow it to be searched through the same interface as the EEBO encoded texts.

  • Titles requested by users at partner institutions are placed at the head of the production queue.

There is quite a lot of Latin in EEBO, because it was in some ways considered a prestige language in the earlier early modern period. Many early printed books were in Latin, so it is generally unsurprising that there’s a lot of it in the EEBO set. Again, this is not English-the-language but English-The-Place. Curiously, the place of imprinting for Ymadroddion bucheddol ynghylch marwolaeth o waith Dr. Sherlock is listed as “gan Leon Lichfield, i John March yn Cat-Eaten-Street, ag i Charles Walley yn Aldermanbury, […] yn Llundain” [by Leon Lichfield, John March-Eaten-in Cat Street, with Charles Walley in Aldermanbury, London], suggesting that “English” refers to place rather than strictly language- and it gets the following metadata:

Publication Country : England
Language : Welsh


Scotland joins with England in 1603 when James VI, King of Scotland inherits the throne to become James I, King of England, but the two countries remain largely independent states until the Acts of Union in 1707. But would we find examples of Scots in EEBO? Scots, like Welsh, is an example of another localized language, though arguably Scots gets more English influence. Kirk is a nice Scots word meaning ‘church’, and here’s an example from William Dunbar’s The tua mariit wemen and the wedo. And other poems from around 1507:

Screen shot 2013-08-27 at 12.39.11Curiously, this is listed in the records as

Publication Country: Scotland
Language: English

As above, I’m not sure everyone would agree that this is “English”. Nor is it printed in “England”.  But these books (and more) are there as part of Early English Books Online.

[1] Thanks to Jonathan Morris (@jonmorris83), a marketing assistant at Palgrave Linguistics, Alun Withey (@DrAlun), Liz Edwards (@eliz_edw) and Sarah Courtney (@sgcourtney)

Counting things in Early Modern Plays So You Don’t Have To: Type/Token Ratios

If you’re just joining me, I’ve been working on word frequencies of six highly-prototypical lexical items in a corpus of slightly less than 400 Early Modern London plays. I recommend starting with my research notes and then looking at some quick & dirty results.

As I noted in my quick & dirty results, these numbers hadn’t been normalized in any way: it was all raw data. In an effort to move beyond just raw data, I compiled the total number of words in each play in the corpus. I initially was interested in how play length might be a variable over time my corpus, so I graphed that. The bulk of my plays are from the early 1600s, as you can see:

play length

Overall, plays do seem to get longer until about 1600, at which point they start to get shorter again. 1662 looks to be an outlier here, as the plays in a straight line on the far right-hand side are mostly by Margaret Cavendish. (I am currently trying to figure out how to color my graphs by author, so if you have advice on that, please let me know: I’m rather haphazardly teaching myself to graph in R as I go.)

OK, so I have the total number of tokens in each text. What if treated every instance of my prototypical lexical items as a specific type, and plotted them as type/token ratios? Type/token ratios have a bit messy history in corpus linguistics, as they’re mostly used to calculate vocabulary denseness (Type/Token Ratios: what do they really tell us?, Richards 1987 [pdf]), but this would show a ratio of the raw frequency of each lexical item of interest in each play compared to the length of each play, which would normalize my data a bit.

Click to zoom:

type/token ratios

First of all, it’s notable that the lexical-frequency-to-play-length ratio make some pretty clear bell-curve shapes; I haven’t tried to calculate standard deviations of play-length. (I suppose I could do that next.) The average length of an Early-Modern London play in my corpus was 22086.5 words.

It seems that as plays get longer, they’re more likely to use man (and, to some extent, wom*n) in ways that are not true for lord/lady and knave/wench. It’s also worth looking at scales here: there are nearly double the number of lords than ladys, although man/woman and knave/wench are more comparable. Also,  there are way fewer instances of knave and wench in my corpus overall, which suggests that maybe these words are not nearly as popular as we might like to think.

Counting things in Early Modern Plays So You Don’t Have To: Some Quick & Dirty Results

I was given a corpus of 400 plays for my PhD on gender in Early Modern London plays. Up to this point I had previously been focusing largely on Shakespeare, but have recently been moving into the larger corpus. So what does one do with 400 plays? My solution was “get to know them a little bit.”  I was counting the raw frequencies for lord/lady, man/wom*n, and knave/wench in the entire corpus using AntConc, manually recording it, and then transcribing this data into a spreadsheet. I had selected these terms on the basis that I had recently spent a lot of time looking at likely collocates for these terms, as these binaries represent a high-, neutral-, and low-  formality distinction.

Several of my twitter followers asked why I was just looking at wom*n and not also m*n, and the answer is that without a regular expression I was going to get a fair quantity of noise from m*n (including but certainly not limited to man, men, mean, moon, maiden, maintain, morn, mutton…). Wom*n, I had found, was a highly successful use of a wildcard, only picking up woman and women in the corpus. While this category remains somewhat imbalanced, it presents a pretty clear scope of the quantities for more neutral forms. Now that I have a better sense of what my corpus is like beyond “those files in that folder on my computer”, I can always go back and get other information pretty easily.

What can we learn from a corpus of 400 plays?
For starters, there’s not actually 400 plays in the 400-play-corpus, but 325 plays. I knew when I started this project that this corpus was less than 400, and that it did not cover everything. It is a representative corpus, but I was a bit surprised at how much less than 400 plays I actually had. These 325 plays cover 53 individual authors from the years 1514-1662,* which looks like this:


Each dot represents a year of publication. You will note that some authors are more represented than others (Shirley, for example, has 33 plays in the corpus, spanning a number of years, whereas someone like Beza has only one play in the corpus.) The average year for a play to be published was in 1613, and an overwhelming majority of these plays have been published in the late 1500s into the first half of the 1600s.

Once I had the raw frequencies for everything, I was curious to see how these terms performed diachronically. For ease I’m going to keep calling it the 400-play corpus, and as you’re reading, remember that this is very quick & dirty. There’s a lot more to say & do with this data, but I think talking about raw data is a useful endeavor in that speaks volumes about the sample itself.

lady lord (diachronic)-1

man woman diachronic-1

These graphs suggest that the use of lady and wom*n look more frequent in the corpus from the late 1500s onwards (they’re both almost in a parabola shape) whereas the use of lord and man begins to decline around 1600, creating more of a bell curve effect.

And what about knave and wench? We see there’s a distinct decrease in usage for both just after the early 1600s, though knave was more frequent earlier in the corpus:knave wench diachronically-1

Two of these three sets of binaries show very similar graphs, but that’s because this is raw data: there’s simply more instances of plays occurring around the late 1530s onwards.

This was my first time using R for any graphing ever, so I’m going to dive back in and see what I can do with a more normalized corpus next.

Additionally, I owe a great debt to the following people, who were very selfless and helpful:
Sarah Werner, Julia Flanders, Shawn Moore, Douglas ClarkSimon Davies. Thank you.

Counting gender-specific nouns in 400 plays so you don’t have to: research notes

Those of you following me on Twitter will have noticed I’ve been tweeting bite-sized facts about gender in Early Modern London plays. Here are some research notes on what I’ve been doing.

The Corpus.
I have a corpus of ~400 Early Modern London plays, culled from EEBO by someone who is not me, spanning from 1514 to 1662. This almost certainly does not cover every play written in that time, nor does it cover variant editions of these plays. This is meant to be a largely representative corpus: I have all major playwrights, a number of minor ones, and most (but importantly not all) plays written by them; one edition per play. These files have been labeled by canonical generic description (eg comedy, tragedy, history, tragicomedy), year of publication, abbreviated author surname, and a truncated version of the title. All of this metadata has been collected from EEBO, again by that same someone who is not me.

The files themselves have had everything but the words said by characters stripped out. There are no headers (no scene/act denotations) and no character markers. Each word is on its own line, and all spelling has been modernized. Here is a sample, from Kyd’s The Spanish Tragedy:
The Spanish Tragedy
This, you will note, is not ideal for reading by human eyes. But computers can do some wonderful things with this format.

I’ve been sorting these files into separate folders by author, to get a sense of how many and which plays by which authors I have in my corpus. This is, quite simply, a little more manageable than a running list of plays sorted by genre & date. It also gives me a larger sense of when these authors are working, what generic kinds of plays I have for them, and allows me to have the flexibility to group them in a variety of ways (playhouses associated with specific playwrights, authors who are contemporaries, etc) later on.

My present goal is to get counts of how many times the words lord/lady, man/wom*n, and knave/wench appear in each play in my corpus. Part of the reason I’ve chosen these terms is that they represent a shift from high – neutral – low formality while retaining gender-specific contexts. I could have chosen other ones: I’ve been looking at collocational patterns in Shakespeare using these terms (here are the relevant slides, .pdf) and wanted to get a sense of how these terms are represented in the larger corpus before I do anything else.

I consider this “getting to know my plays” because I’ve been reading as many of these plays as I possibly can, but I have several disadvantages here:
1. I can remember what many of these plays are about, but not the fine level of detail the computer can pull out for me.

2. Some of these plays are very hard to find in print (and, as I’ve shown, they are not in an ideal format for reading). My university no longer subscribes to EEBO, so I don’t actually have access to the original full-text files.

Getting Data on 400 Plays and What To Do With It.
I’ve been running the plays through a concordance program called AntConc to get a visualization of where and how many of these terms appear in each subcorpus of author. Here’s what Dekker looks like in Antconc’s Concordance Plot viewer:
Screen shot 2013-04-28 at 2.22.44
Each black line represents one instance of the search term, and is visualized in a linear way (so, from the beginning to end of each play). This is useful in that the software  will give me a number of hits in each play AND shows me where these words appear in the play-texts. For The Honest Whore, Part 1, there’s a few instances of “lady” all at once, at the beginning of the play, a few scattered in the middle, another small clump (probably representing a conversation) in the middle, and a few sparse other instances toward the end of the play.  I’m doing this mostly to get a sense of where these highly salient words appear and don’t appear in ways that are very hard to keep track of when you’re reading 400 plays in a traditional, linear fashion. These are words you’d (presumably) expect to find in Early Modern plays, so you’re not really paying much attention to them as a reader.

I record this data by hand in a notebook by author and then manually copy the information into a csv file. While it would be great to essentially have a spreadsheet of all of this information automatically produced, spreadsheets are also not particularly well-designed for human eyes to read. Eventually this will turn into a very nice graph, I’m sure, but in this format, it’s hard to make much sense of it all:
Screen shot 2013-04-28 at 2.38.55

This is admittedly a little easier:
Scan 4

There is an easier way to do this for every my entire corpus at once in R and – presumably – Python, but quite frankly that would become information overload very quickly. So while some of you more computational people may be wondering why I’m moving at such a seemingly glacial pace, the answer is “because I want to be comfortable with the data and familiar in a way that allows me to think and reflect on it as it comes”, rather than having it all at once. I want to get to know my corpus a little bit more first. Eventually, I’ll be moving into R with this data – but not yet.

When I’m done I will be making the csv file available, and will hopefully be posting a write-up here. Thanks for your patience. In the meantime, here’s the csv file for all of Shakespeare (from the Globe Shakespeare, 1841) organized by genre (comedy, history, late plays, tragedies).