Wine Dark Sea

Blog

  • Tokens of Impersonation in Dekker’s City Comedies

    In sixteenth- and seventeeth-century England, the relationship between clothing and identity was complex. As Ann Rosalind Jones and Peter Stallybrass have shown, the fact that clothing circulated as currency among different owners implicitly called into question its supposed correspondence with the wearer’s social and financial status. Stephen Orgel has explored how issues surrounding clothing and identity played out on the Elizabethan and Jacobean stage—a place where clothing was understood at once as the defining token of identity and as disguise, where audiences entered into the fiction that a dress could temporarily transform a lower-class boy into a noble woman. The possibility that appearance might not match reality was problematic for early modern audiences, however, because the English credit culture that emerged in this period depended on people’s ability to assess one another’s presentations of honesty and trustworthiness. By challenging the assumed correspondence between social performance and identity, cross-dressing figures like Moll Cutpurse in Dekker and Middleton’s The Roaring Girl (1611) suggest the fallability of a system in which a person’s economic status is inferred from his or her appearance.

    I wondered whether The Roaring Girl’s concern with the instability of credit might be visible at the linguistic level. In Witmore and Hope’s “very large dendrogram” (see Figure 9 here), three plays group tightly with The Roaring Girl: Westward Ho (Dekker and Webster, 1604), Northward Ho (Dekker and Webster, 1605), and The Honest Whore, Part 2 (Dekker, performed 1605 and published 1630). Based on where they cluster in the dendrogram, it is clear that these texts are not merely linked by authorship, genre, or time period. I hypothesized that these four plays might all share The Roaring Girl’s concern with disguise and credit, and that this concern would be one of the factors linking them together stylistically. Still, much of early modern drama, especially city comedy, is concerned with the economics of identity. Assuming that these plays’ treatment of credit and disguise contributes to their linkage, what is uniquely similar about them that pushes the plays together?

    To answer this question, I performed Principle Component Analysis (PCA) on 130 plays performed between 1601 and 1621 and found a component that united the plays on The Roaring Girl twig. As it turns out, the cocktail of linguistic factors that joins these four plays includes the categories Docuscope labels “Person Properties” and “Sense Objects.” The component also discriminates against Positive and Negative Standards, Abstract Concepts, and Negativity.

    The passage from the four plays that is most exemplary of this component comes from Westward Ho. Words underlined in purple are Person Properties, while bright yellow indicates Sense Objects:

    In this scene, the bawd Birdlime tries to protect the identity of one of her clients, Tenterhook, from another who has entered her house. Tenterhook hides in a closet with the prostitute, Luce, and covers her eyes. She tries to identify him by the feel of his hands and what he wears on them. In guessing, she reveals the names of all her clients, thereby contradicting the bawd’s claim that whores practice a kind of doctor-patient confidentiality. The most frequent elements in this scene are Person Properties, Sense Objects, Questions and Direct Address. In other words, in this scene characters address one another based on their perceived identities (mistress, captain) and their interactions with the physical world.

    The second most exemplary passage, this time from The Roaring Girl, is even more explicitly concerned with clothing. Here again, purple indicates words tagged as Person Properties, and yellow highlights Sense Objects:

    In  this scene, Moll’s man Trapdoor reports to Sir Alexander about his mistress, and they hatch a plan to catch her in flagrante delicto with Alexander’s son Sebastian. Again, the passage is dominated by Person Properties (linked mainly to gender and social position) and Sense Objects. Moll’s male apparel is thoroughly catalogued, and the interplay of the repeated terms “girl,” “mistress,” and “man”/ “male” highlights the instability of her identity when she wears these typically masculine items of clothing. The rapid-fire comedic exchange amplifies the effect of the patterns—for example, the repeated pun on “shirt of mail” / “male shirt” creates a glut of Person Properties and Sense Objects in those lines.

    It would seem, then, that the component under consideration selects for descriptions of people—their social roles (Person Properties) and the way they dress (Sense Objects)—as well as descriptions of the material world. What does PC2 select against? The least exemplary passage comes from a scene in The Roaring Girl in which Sebastian attempts to persuade his father that Moll is a chaste woman, despite her propensity for brawling and wearing men’s clothing. In this passage, green indicates Positive Standards and Negative Standards; light purple flags Abstract Concepts and various narrative cues such as Reporting Events; and orange highlights Negativity as well as other indicators of interiority such as Subjective Perception:

    Sebastian explicitly critiques his father for judging Moll by her appearances; yet the language of this passage is very different from previous ones in which the obsession with appearances and roles was implicit in the preponderance of Person Properties and Sense Objects. Here, the most common elements are Positive and Negative Standards, Abstract Concepts, and Negativity. Given that this passage is the opposite of the component that grouped these four plays together, it would seem that this particular combination of standards, judgment, and interior life is uncommon in the world of these plays.

    While the component that sets these four texts apart selects for plays about sex and clothing, it is not merely a “disguise plot” component. Given its opposition to standards and interiority, it might be more broadly defined as language that explores the material world’s inability to accurately reflect abstract truths. I believe this component can show us something about Dekker’s engagement, not only with identity, but with credit culture. In selecting for moments where people are described based on their clothing, appearance, and/or social role, and selecting against value judgments of those people’s performances, this component might highlight plays that represent the impossibility of assessing people based on their public personae. Not only might a woman dress as a man, but a prostitute might present herself as a rich woman, provided she has wealthy enough customers. Similarly, an insolvent gallant might dress well to trick shopkeepers into extending him credit (or their wives into sleeping with him). The fact that Dekker’s treatment of disguise excludes judgments, standards, or appeals to authority suggests that his critique is not of the amorality of the city. Rather, it is of the way that credit relations punish perceived immorality, while often rewarding well-hidden immorality. This explanation might help explain why these particular plays cluster together, rather than blending in with all the rest of Jacobean city comedy.

    Richard Wawso argues that all Jacobean drama, through its concern with disguise, counterfeit, and crime, invites audiences to question the credibility of their neighbors. Certainly, Dekker’s stage comedies reflect a sustained interest in the unstable relationship between dress and character, but as this component reveals, they do so in a unique way. I hope my findings might help us begin to understand how different writers’ attitudes toward these issues register at the linguistic level, even when they use the same stock of plot points and characters. While a morally conservative writer like Jonson might condemn the coney-catchers and cross-dressers of the London underworld for wreaking havoc on the institutions of credit that undergird social commerce, Dekker seems more critical of the credit system itself. In its very structure—in its reliance on appearances—the system invites exploitation by those who are willing to play the game. We are able to see this critique coming through in these plays because, like an expert coney-catcher, Docuscope counts the tokens of texts’ identities, registering the affinities that are alternately hidden and revealed by the linguistic “clothing” they wear.

  • Finding the Sherlock in Shakespeare: some ideas about prose genre and linguistic uniqueness

    An unexpected point of linguistic similarity between detective fiction and Shakespearean comedy recently led me to consider some of the theoretical implications of tools like DocuScope, which frequently identify textual similarities that remain invisible in the normal process of reading.

    A Linguistic Approach to Suspense Plot

    Playing around with a corpus of prose, we discovered that the linguistic specs associated with narrative plot are surprisingly unique. Principle Component Analysis performed on the linguistic features counted by DocuScope suggested the following relationship between the items in the corpus:

    I interpreted the two strongest axes of differentiation seen in the graph (PC 1 and PC 2) as (1) narrative, and (2) plot. The two poles of the narrative axis are Wuthering Heights (most narrative) and The Communist Manifesto (least narrative). The plot axis is slightly more complicated. But on the narrative side of the spectrum, plot-driven mysteries like “The Speckled Band” and The Canterville Ghost score high on plot, while the least plotted narrative is Samuel Richardson’s Clarissa (9 vols.). For now, I won’t speculate about why Newton’s Optics scores so astronomically high on plot. It is enough that when dealing with narrative, PC 2 predicts plot.

    The fact that something as qualitative and amorphous as plot has a quantitative analogue leads to several questions about the meaning of the data tools like DocuScope turn up.

    Linguistic Plot without Actual Plot

    Because linguistic plot is quantifiable, it allows us to look for passages where plot is present to a relative degree. Given a large enough sample, it is more than likely that some relatively plotted passages will occur in texts that are not plotted in any normal sense. This would at minimum raise questions about how to handle genre boundaries in digital literary research.

    Our relative-emplotment test (done in TextViewer) yielded intuitive results when performed on the dozen or so stories in The Adventures of Sherlock Holmes: the passages exhibiting the strongest examples of linguistic plot generally narrated moments of discovery, and moved the actual plot forward in significant ways. Often, these passages showed Holmes and Watson bursting into locked rooms and finding bodies.

    When we performed the same test on the Shakespeare corpus, something intriguing happened. The passages identified by TextViewer as exhibiting linguistic plot look very different from the corresponding passages in Sherlock Holmes. There were no dead bodies, no broken-down doors, and no exciting discoveries. Nonetheless, the ‘plotted’ Shakespeare scenes were remarkably consistent with each other. Perhaps most significant in the context of their genre, these scenes had a strong tendency to show characters putting on performances for other characters. Additionally, in a factor that is fascinating even though it is probably a red herring, the ‘plotted’ Shakespeare scenes had an equally strong tendency to involve fairies.

    The consistent nature of the ‘plotted’ Shakespeare scenes suggests that the linguistic specs associated with plot when they occur in Sherlock Holmes may have different, but equally specific, effects in other genres. The next step would be to find a meaningful correspondence between the two seemingly disparate literary devices that accompany linguistic plot – detectives bursting into rooms to solve murders, and plays within plays involving fairies. I have some hunches about this. But in many ways the more important question is what is at stake in using DocuScope to identify such unexpected points of overlap.

    Enough measurable links between seemingly unlike texts could suggest an invisible web of cognates, which share an underlying structure despite their different appearances and literary classifications. Accordingly, we might hypothesize that reading involves selective ignorance of semantic similarities that could otherwise lead to the socially deviant perception that A Midsummer Night’s Dream resembles a Sherlock Holmes mystery.

    The question, then, is this: if the act of reading consists in part of ignoring unfruitful similarities, then what happens when these similarities nonetheless become apparent to us? Looking back at the corpus graph, we begin to see all sorts of possibilities, many of which would be enough make us literary outcasts if voiced in the wrong company. Could Newton’s Optics contain the most exciting suspense plot no one has ever noticed? Could Martin Luther be secretly more sentimental than Clarissa?

    Estranging Capacities of Digital Cognates

    I have been using the term ‘cognate’ to describe the relationship between linguistically similar but otherwise dissimilar texts. These correspondences will only be meaningful if we can connect them in a plausible way to our readerly understanding of the texts or genres in question. In the case of detective fiction and Shakespearean comedy, this remains to be seen. But our current lack of an explanation does not mean we should feel shy about pursuing the cognates computers direct us to. My analogy is the pop-culture ritual of watching The Wizard of Oz, starting the Pink Floyd album Dark Side of the Moon on the third roar of the MGM lion. The movie and the record sync up in a plausible pattern, prompting the audience to grasp a connection between the cognate genres of children’s movies and psychedelic rock.

    If digital methods routinely direct our attention to patterns we would never notice in the normal process of reading, then we can expect them to turn up a large number of such cognates. If we want to understand the results these tools are turning up, we should develop a terminology and start thinking about implications – not just for the few correspondences we can explain, but also for the vast number we cannot explain, at least right now.

  • Why the Difference? Accounting for Variation between the Folio and Globe Editions of Shakespeare’s Plays

    To what extent is modern text analysis software capable of dealing with historical data? This is a perennial question asked by those working with digitized historical texts who wish to see how an analysis of such texts can be facilitated by cutting-edge technologies. No doubt the best way to answer the question is to test this software with two versions of the same text, where one version of the text can be considered an older and noticeably different version than the other version.

    Enter the Folio and Globe editions of Shakespeare’s plays. The latter was published in 1867 and contains modernized spelling throughout, whereas the former was published in 1623 and maintains the original spelling of Shakespeare’s Early Modern English. Using DocuScope for text analysis and JMP for statistical visualizations, the following dendrogram was created:

    The texts highlighted in red are from the Folio edition, whereas the texts highlight in blue come from the Globe edition. One would expect all of Shakespeare’s Folio plays to cluster with their Globe complement here. Much Ado About Nothing is Much Ado About Nothing, after all, regardless of which edition it appears in. But for the most part, this neat pairing off is not what happens: instead, most of the Folio plays are grouping with other Folio plays, and the same is true for the Globe plays. Only a few plays are actually grouping with themselves at the top of the dendrogram. Methinks we have a problem.

    Upon closer inspection, I found that 13,667 items were tagged by DocuScope in the Globe edition of Much Ado, but only 11,382 items were tagged in the Folio edition of the same play: a 16.7% difference. An inspection of eleven other Shakespeare plays provides us with an overall mean difference of 17.8%: a difference that cannot be considered good when it comes to tagging accuracy.

    But why the disparity? Maybe a closer look at DocuScope can give us an idea.

    First the Folio version of the opening scene in Much Ado About Nothing (with the “Interior Thought” and “Public Values” clusters turned on):

    And the Globe version of the same scene with the same clusters turned on:

    One need not read far to discover what’s (not) being tagged in the older, Folio edition of Much Ado: Learne versus learn. It appears the orthographic rendering of the unstressed final –e is causing DocuScope to overlook this work altogether. We find the same mistake later on with indeede/indeed, kindnesse/kindness, helpe/help, and kinde/kind. Another common problem is Early Modern use of u, which is rendered v in modern orthography: deseru’d vs. deserved, seruice vs. service,  and ouerflow vs. overflow. There are also a few punctuation issues causing problems: the use of apostrophe (as we see in deseru’d) and the use of | (con | flict vs. conflict), which probably results from some sort of scanning or other computer error. In other plays, the hyphen was also found to be a possible culprit of DocuScope overlooking certain items (ouer-charg’d vs. overcharged).

    Although the overall number of DocuScope omissions on a Folio play is rather large, the actual number of error types is quite small. This gives us hope that, with a bit of modification, it may well be possible to train DocuScope to read non-modern(ized) texts.

     

  • The comic ‘I’ and the tragic ‘we’?

    In our Shakespeare Quarterly paper, we used Docuscope to come up with a description of Shakespeare’s comic language which centres on the rapid exchange of singular pronouns: I/you and my/your. We claimed there that Shakespearean comedies typically involve people arguing about things, striving to arrive at a ‘we’ of agreement, but not being able to until the final scene. Here’s what we said in more detail (we’re discussing Twelfth Night):

    The quick trading of I/you and my/your strings in Comic dialogue suggests a world in which predicates are attached to subjects from two, and only two, points of view. This is not a universe of one; nor is it a crowd. It is not surprising that Comic plotting, built as it is on sexual pairings, would favor this type of bivalent, perspectival tagging of action by speakers. But there is something else going on here. Olivia is trying to make something happen in this exchange. She says, “do not extort thy reasons from this clause,” and earlier, “I would you were as I would have you be!” (3.1/1392, 1381). The “thy” and “you” are important because the speaker is trying to create or assert a particular interpretation of how these two individuals relate to one another (and the words exchanged between them). The essential drama in this situation is the asymmetry of desire that obtains between the two characters, an asymmetry that keeps Viola from assenting to Olivia’s advances. That resistance is actually what forces Olivia to make these statements that are rich with I/you and me/my, since she uses these words as anchors for a broader interpretation that does not yet obtain. She really wants to say we. And Cesario doesn’t, so they remainin I/you dialogue…

    Shakespeare writes Comedies in which characters, sometimes quite perversely, find the wrong way to the ones they love. Often it is chance or an onstage helper who sorts this out. Shakespeare is actually quite reserved when it comes to showing love as naturally progressing through its obstacles unassisted. But given that in the initial stages of courtship Shakespearean lovers almost never meet and join in a perfectly symmetrical way—they don’t start out as stones set in an arch, leaning perfectly on a keystone—we should expect this asymmetry to show itself in the language. Where does it show up? It appears when a resistant individual, a “you,” prevents another “I” from arriving at an interpretation of a relationship that might be referred to as a “we” before others. Let’s call this the “resistant-you” hypothesis. Linguistically, the effect manifests itself in the assertion of the self (“FirstPerson”) and the rejection of suggested mental and emotional realities (“DenyDisclaim”).

    We’ve been finding that high frequencies of first person pronouns, and other features associated with rapid dialogue, are characteristic of most types of Early Modern comedy. But what of the implied correlative to this? If comedies are the genre of ‘I’; are tragedies the genre of ‘we’?

    A quick way to test this is to use Martin Mueller et al.’s excellent Wordhoard tool to run a log likelihood vocabulary test on Shakespeare’s comedies and tragedies. This type of test takes an analysis corpus (in this case Shakespeare’s comedies), and compares it to a reference corpus (Shakespeare’s tragedies). The output flags those words that are either more or less frequent in the analysis corpus than we would expect, given the frequencies found in the reference corpus.

    The results in this case are as follows:

     

    What we are interested in here is the list of lemmas in column 1: ‘she’, ‘I’, ‘master’, ‘a’, ‘sir’ etc; and the symbol in column 3 ‘Relative use’ – which tells us if the frequency is greater (+) or less (-) than expected. (Column 4 gives the log likelihood value, and a number of asterisks indicating degree of statistical significance, but all the results we are looking at here are highly significant, so we can ignore this.)

    Behold: pronouns used more in the comedies than the tragedies are the singular ‘she’, ‘I’, ‘you’ (let’s assume these are mainly singular uses) – these are all marked + in column 3. Now look at the results for the plural pronouns ‘our’, ‘we’, ‘they’: all marked -, and so lowered in the comedies/raised in the tragedies.

    This is a very strong finding (especially considering how frequent pronouns are), and it invites further exploration of the dialogic nature of comedy in comparison with the communal nature of tragedy.
    jh/29.7.2011

  • Phylogenetic inference

    Phylogenetic inference

    Image by Greg McInerny and Stefanie Posavec – textual shifts between editions of Darwin’s Origin of Species (used by kind permission of the artist – see bottom of post for further details).

     

    In advance of starting up some big experiments on the texts being made available by TCP, we’ve been discussing the models developed in mathematics/biology for tracing influence.

    This began with a conversation with David Krakauer from the Sante Fe Institute about our work. He works in mathematics and evolutionary biology, and has collaborated a bit with Franco Moretti. We told him about our attempts to group texts by genre and then trace their linguistic predecessors and descendants. He suggested this was similar to the problem of phylogenetic inference in biology.

    The problem as we currently understand it: we identify a group of texts within a population based on manifest traits known to humans; we then want to account for the development of these traits among items understood to be earlier in the sequence; we link these traits to sentence level linguistic items; we track the traits via these items.

    We are thinking about this, since this is going to be one of our big intellectual problems once we add time to the analysis (so far, we’ve been looking at populations, e.g. Shakespeare’s plays, in pretty much the same time slot).

    Here are some starter references (though they are certainly not entry-level in all cases!):

    >> http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2567038/
    >> http://en.wikipedia.org/wiki/Bayesian_inference_in_phylogeny
    >> http://fontana.med.harvard.edu/www/Documents/WF/Papers/theory.pdf

    In our work to date, we have tried to think carefully about the philosophical and methodological implications of what we are doing, rather than simply focus on the (admittedly often attractive and very interesting) results – so it is important for us to consider the implications of taking on models from other fields (especially when those other fields are better developed than ours).

    Jonathan Hope has done some work on biology and linguistic history in the past. And one thing that might make the application of biological models difficult is the different natures of inheritance and ‘traits’ in language and biology. In biology, traits (or the genes that produce them) have to be passed down in a closed, continuous way.  We get our genes from our parents, not some random person we bump into on the tube – and it’s impossible for us to naturally acquire a gene, however useful it might be, from another species.

    None of this holds for language though. If we’re writing a ‘history’, we’ll want to borrow some traits from other histories – but we don’t have to take everything from other histories, and we can take traits from pretty much any histories we happen to have read: old, recent, famous, unknown. So the status of *generic* traits is very different to *genetic* ones.

    In addition, we are not confined to our own linguistic *species*. If we want, we can introduce traits from a completely different species to produce something new. In language, if you want a bat, you can cross rats with sparrows. In biology, you have to wait for one to evolve.

    Once we are thinking about genres developing over time, it will be easy for us to assume a biological model of linear generations and influence. It’s a useful way of thinking, and the statistical techniques are powerful, but we’ll need to remember that we aren’t looking at exactly the same kind of process.

    A further consideration is the power of the inferencing techniques that have been developed in biology. It is very tempting to want to throw these at our newly available textual data – but one very significant thing to have emerged from our work is the importance of having understandable observations.

    If a statistical black box tells you some fact, that is not as interesting or important as being able to understand where a particular thing comes from and how it got there. If some fancy inference algorithm tells you there’s a pattern, it isn’t that helpful unless you can comprehend it, since an incomprehensible or inexplicable pattern might just be an artefact of the process or analysis.

    With biology, the models are more well known and trusted, so an incomprehensible pattern is more easy to accept: it could more safely be taken as an indication of a real effect we just haven’t understood yet.

    Ultimately, our interest is in building tools to help people understand the complexity in texts. We are less interested in having machines sort it out automatically (indeed, we are probably sceptical that this is really possible). Although, there is also a need for tools to help people sort out what the machines figure out…

     

    References

    Jonathan Hope,‘Rats, bats, sparrows and dogs: biology, linguistics and the nature of Standard English’ in The Development of Standard English 1300-1800, Laura Wright (ed.), pp. 49-56, (Cambridge University Press: 2000)  ISBN 0-521-77114-5

    Stefanie Posavec and Greg McInerny:  The (En)tangled Word Bank project (originated at Microsoft Research, Cambridge)

    http://research.microsoft.com/en-us/people/a-gregmc/

    http://research.microsoft.com/en-us/projects/TextVis/

    http://www.itsbeenreal.co.uk/index.php?/on-going/chapter-close-ups/