Tag Archives: Digital Arts & Humanities

Collocations in Modernist Prose

Screen Shot 2017-07-24 at 14.51.47I have recently begun to experiment with Natural Language Processing to determine how particular words in modernist texts are correlated. I’m still getting my head around Python and NLTK, but so far I’m finding it much more user-friendly than similar packages in R.

Long-term I hope to graph these collocations in high-vector space, so that I can graph them, but for the moment, I’m interested in noting the prevalence of the term ‘young man’, Self and Baume being the only authors that have female adjective-noun phrases, and the usage of titles which convey particular social hierarchies; Joyce, Woolf and Bowen’s collocations are almost exclusively composed of these, as is Stein’s, with the clarifier that Stein’s appear shorn of their ‘Mr.’, ‘Miss.’ or ‘Doctor’.

Here’s all the collocations in the modernist corpus:

young man; robert jordan; new york; gertrude stein; old man; could see; henry martin; every one; years ago; first time; long time; hugh monckton; great deal; come back; david hersland; good deal; every day; edward colman; came back; alfred hersland

Canonical modernist texts:

young man; robert jordan; gertrude stein; henry martin; new york; every one; old man; could see; years ago; long time; hugh monckton; first time; great deal; david hersland; come back; good deal; every day; edward colman; alfred hersland; mr. bettesworth

Contemporary texts, Enright, Self, Baume, McBride:

fat controller; phar lap; von sasser; first time; per cent; could see; old man; one another; even though; years ago; new york; front door; young man; either side; someone else; dave rudman; last night; living room; steering wheel; every time

Djuna Barnes

frau mann; nora said; english girl; someone else; long ago; leaned forward; london bridge; come upon; could never; god knows; doctor said; sweet sake; first time; five francs; terrible thing; francis joseph; hôtel récamier; orange blossoms; bowed slightly; would say

Eimear McBride

kentish town; someone else; first time; last night; jesus christ; something else; years ago; five minutes; every day; hail mary; take care; next week; arms around; never mind; every single; little girl; little boy; two years; soon enough; come back

Elizabeth Bowen

mrs kerr; lady waters; mrs heccomb; major brutt; mme fisher; lady naylor; miss fisher; good deal; said mrs; first time; lady elfrida; one another; young man; colonel duperrier; aunt violet; last night; ann lee; one thing; sir robert; sir richard

Ernest Hemingway

robert jordan; old man; could see; colonel said; gran maestro; catherine said; jordan said; richard gordon; long time; pilar said; thou art; pablo said; nick said; bill said; girl said; captain willie; young man; automatic rifle; mr. frazer; david said

F. Scott FitzGerald

new york; young man; years ago; first time; sally carrol; several times; fifth avenue; ten minutes; minutes later; richard caramel; thousand dollars; five minutes; young men; evening post; old man; next day; saturday evening; long time; last night; come back

Gertrude Stein

gertrude stein; every one; david hersland; alfred hersland; angry feeling; family living; independent dependent; jeff campbell; julia dehning; mrs. hersland; daily living; whole one; bottom nature; madeleine wyman; good deal; mary maxworthing; middle living; miss mathilda; mabel linker; every day

James Joyce

buck mulligan; said mr.; martin cunningham; aunt kate; says joe; mary jane; corny kelleher; ned lambert; mrs. kearney; stephen said; mr. henchy; ignatius gallaher; father conmee; nosey flynn; mr. kernan; myles crawford; cissy caffrey; ben dollard; mr. cunningham; miss douce

Marcel Proust

young man; faubourg saint-germain; long ago; caught sight; first time; every day; one day; great deal; des laumes; young men; could see; quite well; next day; one another; would never; nissim bernard; victor hugo; would say; louis xiv; long time

Samuel Beckett

said camier; said mercier; miss counihan; lord gall; miss carridge; mr. kelly; panting stops; said belacqua; mr. endon; said wylie; said neary; one day; otto olaf; dr. killiecrankie; come back; vast stretch; mrs gorman; push pull; something else; ground floor

Sara Baume

even though; tawny bay; living room; old man; passenger seat; bird walk; maggot nose; shut-up-and-locked room; stone fence; food bowl; lonely peephole; low chair; old woman; kennel keeper; rearview mirror; shih tzu; shore wall; safe space; every day; oneeye oneeye

Virginia Woolf

miss barrett; mrs. ramsay; mrs. hilbery; young man; st. john; could see; years ago; peter walsh; mrs. thornbury; miss allan; said mrs.; young men; mrs. swithin; human beings; wimpole street; mrs. flushing; mr. ramsay; mrs. manresa; sir william; door opened

Anne Enright

new york; per cent; eliza lynch; dear friend; years old; even though; first time; came back; years ago; long time; michael weiss; señor lópez; living room; every time; looked like; could see; one day; said constance; pat madigan; mrs hanratty

Will Self

fat controller; phar lap; von sasser; one another; old man; could see; first time; per cent; dave rudman; let alone; front door; young man; skip tracer; quantity theory; jane bowen; los angeles; young woman; either side; charing cross; long since

Flann O’Brien

father fahrt; good fairy; father cobble; said shanahan; mrs crotty; said furriskey; said lamont; mrs laverty; one thing; sergeant fottrell; said slug; old mathers; public house; far away; cardinal baldini; monsignor cahill; mrs furriskey; red swan; black box; said shorty

Ford Madox Ford

henry martin; hugh monckton; edward colman; privy seal; mr. bettesworth; mr. fleight; young man; mr. sorrell; sergius mihailovitch; young lovell; new york; jeanne becquerel; lady aldington; kerr howe; anne jeal; miss peabody; mr. pett; great deal; marie elizabeth; robert grimshaw

Jorge Luis Borges

ts’ui pên; buenos aires; pierre menard; eleventh volume; richard madden; nils runeberg; yiddische zeitung; stephen albert; hundred years; erik lönnrot; firing squad; henri bachelier; madame henri; orbis tertius; vincent moon; paint shop; seventeenth century; anglo-american cyclopaedia; fergus kilpatrick; years ago

Joseph Conrad

mrs. travers; mrs verloc; mrs. fyne; peter ivanovitch; doña rita; miss haldin; mrs. gould; assistant commissioner; charles gould; san tomé; chief inspector; years ago; captain whalley; could see; van wyk; old man; dr. monygham; gaspar ruiz; young man; mr. jones

D.H. Lawrence

young man; st. mawr; mr. may; mrs. witt; blue eyes; miss frost; could see; one another; mrs bolton; ‘all right; come back; said alvina; two men; of course; good deal; long time; mr. george; next day

William Faulkner

uncle buck; aleck sander; miss reba; years ago; dewey dell; mrs powers; could see; white man; four years; old man; ned said; division commander; general compson; miss habersham; new orleans; uncle buddy; let alone; one another; united states; old general

Literary Cluster Analysis

I: Introduction

My PhD research will involve arguing that there has been a resurgence of modernist aesthetics in the novels of a number of contemporary authors. These authors are Anne Enright, Will Self, Eimear McBride and Sara Baume. All these writers have at various public events and in the course of many interviews, given very different accounts of their specific relation to modernism, and even if the definition of modernism wasn’t totally overdetermined, we could spend the rest of our lives defining the ways in which their writing engages, or does not engage, with the modernist canon. Indeed, if I have my way, this is what I will spend a substantial portion of my life doing.

It is not in the spirit of reaching a methodology of greater objectivity that I propose we analyse these texts through digital methods; having begun my education in statistical and quantitative methodologies in September of last year, I can tell you that these really afford us no *better* a view of any text then just reading them would, but fortunately I intend to do that too.

This cluster dendrogram was generated in R, and owes its existence to Matthew Jockers’ book Text Analysis with R for Students of Literature, from which I developed a substantial portion of the code that creates the output above.

What the code is attentive to, is the words that these authors use the most. When analysing literature qualitatively, we tend to have a magpie sensibility, zoning in on words which produce more effects or stand out in contrast to the literary matter which surrounds it. As such, the ways in which a writer would use the words ‘the’, ‘an’, ‘a’, or ‘this’, tends to pass us by, but they may be far more indicative of a writer’s style, or at least in the way that a computer would be attentive to; sentences that are ‘pretty’ are generally statistically insignificant.

II: Methodology

Every corpus that you can see in the above image was scanned into R, and then run through a code which counted the number of times every word was used in the text. The resulting figure is called the word’s frequency, and was then reduced down to its relative frequency, by dividing the figure by total number of words, and multiplying the result by 100. Every word with a relative frequency above a certain threshold was put into a matrix, and a function was used to cluster each matrix together based on the similarity of the figures they contained, according to a Euclidean metric I don’t fully understand.

The final matrix was 21 X 57, and compared these 21 corpora on the basis of their relative usage of the words ‘a’, ‘all’, ‘an’, ‘and’, ‘are’, ‘as’, ‘at’, ‘be’, ‘but’, ‘by’, ‘for’, ‘from’, ‘had’, ‘have’, ‘he’, ‘her’, ‘him’, ‘his’, ‘I’, ‘if’, ‘in’, ‘is’, ‘it’, ‘like’, ‘me’, ‘my’, ‘no’, ‘not’, ‘now’, ‘of’, ‘on’, ‘one’, ‘or’, ‘out’, ‘said’, ‘she’, ‘so’, ‘that’, ‘the’, ‘them’, ‘then’, ‘there’, ‘they’, ‘this’, ‘to’, ‘up’, ‘was’, ‘we’, ‘were’, ‘what’, ‘when’, ‘which’, ‘with’, ‘would’, and ‘you’.

Anyway, now we can read the dendrogram.

III: Interpretation

Speaking about the dendrogram in broad terms can be difficult for precisely the reason that I indicative above; quantitative/qualitative methodologies for text analysis are totally opposed to one another, but what is obvious is that Eimear McBride and Gertrude Stein are extreme outliers, and comparable only to each other. This is one way unsurprising, because of the brutish, repetitive styles and is in other ways very surprising, because McBride is on record as dismissing her work, for being ‘too navel-gaze-y.’

Jorge Luis Borges and Marcel Proust have branched off in their own direction, as has Sara Baume, which I’m not quite sure what to make of. Franz Kafka, Ernest Hemingway and William Faulkner have formed their own nexus. More comprehensible is the Anne Enright, Katherine Mansfield, D.H. Lawrence, Elizabeth Bowen, F. Scott FitzGerald and Virginia Woolf cluster; one could make, admittedly sweeping judgements about how this could be said to be modernism’s extreme centre, in which the radical experimentalism of its more revanchiste wing was fused rather harmoniously with nineteenth-century social realism, which produced a kind of indirect discourse, at which I think each of these authors excel.

These revanchistes are well represented in the dendrogram’s right wing, with Flann O’Brien, James Joyce, Samuel Beckett and Djuna Barnes having clustered together, though I am not quite sure what to make of Ford Madox Ford/Joseph Conrad’s showing at all, being unfamiliar with the work.

IV: Conclusion

The basic rule in interpreting dendrograms is that the closer the ‘leaves’ reach the bottom, the more similar they can be said to be. Therefore, Anne Enright and Will Self are the contemporary modernists most closely aligned to the forebears, if indeed forebears they can be said to be. It would be harder, from a quantitative perspective, to align Sara Baume with this trend in a straightforward manner, and McBride only seems to correlate with Stein because of how inalienably strange their respective prose styles are.

The primary point to take away here, if there is one, is that more investigations are required. The analysis is hardly unproblematic. For one, the corpus sizes vary enormously. Borges’ corpus is around 46 thousand words, whereas Proust reaches somewhere around 1.2 million. In one way, the results are encouraging, Borges and Barnes, two authors with only one texts in their corpus, aren’t prevented from being compared to novelists with serious word counts, but in another way, it is pretty well impossible to derive literary measurements from texts without taking their length into account. The next stage of the analysis will probably involve breaking the corpora up into units of 50 thousand words, so that the results for individual novels can be compared.

Can a recurrent neural network write good prose?

At this stage in my PhD research into literary style I am looking to machine learning and neural networks, and moving away from stylostatistical methodologies, partially out of fatigue. Statistical analyses are intensely process-based and always open, it seems to me, to fairly egregious ‘nudging’ in the name of reaching favourable outcomes. This brings a kind of bathos to some statistical analyses, as they account, for a greater extent than I’d like, for methodology and process, with the result that the novelty these approaches might have brought us are neglected. I have nothing against this emphasis on process necessarily, but I do also have a thing for outcomes, as well as the mysticism and relativity machine learning can bring, alienating us as it does from the process of the script’s decision making.

I first heard of the sci-fi writer from a colleague of mine in my department. It’s Robin Sloan’s plug-in for the script-writing interface Atom which allows you to ‘autocomplete’ texts based on your input. After sixteen hours of installing, uninstalling, moving directories around and looking up stackoverflow, I got it to work.I typed in some Joyce and got stuff about Chinese spaceships as output, which was great, but science fiction isn’t exactly my area, and I wanted to train the network on a corpus of modernist fiction. Fortunately, I had the complete works of Joyce, Virginia Woolf, Gertrude Stein, Sara Baume, Anne Enright, Will Self, F. Scott FitzGerald, Eimear McBride, Ernest Hemingway, Jorge Luis Borges, Joseph Conrad, Ford Madox Ford, Franz Kafka, Katherine Mansfield, Marcel Proust, Elizabeth Bowen, Samuel Beckett, Flann O’Brien, Djuna Barnes, William Faulkner & D.H. Lawrence to hand.

My understanding of this recurrent neural network, such as it is, runs as follows. The script reads the entire corpus of over 100 novels, and calculates the distance that separates every word from every other word. The network then hazards a guess as to what word follows the word or words that you present it with, then validates this against what its actuality. It then does so over and over and over, getting ‘better’ at predicting each time. The size of the corpus is significant in determining the length of time this will take, and mine required something around twelve days. I had to cut it off after twenty four hours because I was afraid my laptop wouldn’t be able to handle it. At this point it had carried out the process 135000 times, just below 10% of the full process. Once I get access to a computer with better hardware I can look into getting better results.

How this will feed into my thesis remains nebulous, I might move in a sociological direction and take survey data on how close they reckon the final result approximates literary prose. But at this point I’m interested in what impact it might conceivably have on my own writing. I am currently trying to sustain progress on my first novel alongside my research, so, in a self-interested enough way, I pose the question, can neural networks be used in the creation of good prose?

There have been many books written on the place of cliometric methodologies in literary history. I’m thinking here of William S. Burroughs’ cut-ups, Mallarmé’s infinite book of sonnets, and the brief flirtation the literary world had with hypertext in the 90’s, but beyond of the avant-garde, I don’t think I could think of an example of an author who has foregrounded their use of numerical methods of composition. A poet friend of mine has dabbled in this sort of thing but finds it expedient to not emphasise the aleatory aspect of what she’s doing, as publishers tend to give a frosty reception when their writers suggest that their work is automated to some extent.

And I can see where they’re coming from. No matter how good they get at it, I’m unlikely to get to a point where I’ll read automatically generated literary art. Speaking for myself, when I’m reading, it is not just about the words. I’m reading Enright or Woolf or Pynchon because I’m as interested in them as I am in what they produce. How synthetic would it be to set Faulkner and McCarthy in conversation with one another if their congruencies were wholly manufactured by outside interpretation or an anonymous algorithmic process as opposed to the discursive tissue of literary sphere, if a work didn’t arise from material and actual conditions? I know I’m making a lot of value-based assessments here that wouldn’t have a place in academic discourse, and on that basis what I’m saying is indefensible, but the probabilistic infinitude of it bothers me too. When I think about all the novelists I have yet to read I immediately get panicky about my own death, and the limitless possibilities of neural networks to churn out tomes and tomes of literary data in seconds just seems to me to exacerbate the problem.

However, speaking outside of my reader-identity, as a writer, I find it invigorating. My biggest problem as a writer isn’t writing nice sentences, given enough time I’m more than capable of that, the difficulty is finding things to wrap them around. Mood, tone, image, aren’t daunting, but a text’s momentum, the plot, I suppose, eludes me completely. It’s not something that bothers me, I consider plot to be a necessary evil, and resent novels that suspend information in a deliberate, keep-you-on-the-hook sort of way, but the ‘what next’ of composition is still a knotty issue.

The generation of text could be a useful way of getting an intelligent prompt that stylistically ‘borrows’ from a broad base of literary data, smashing words and images together in a generative manner to get the associative faculties going. I’m not suggesting that these scripts would be successful were they autonomous, I think we’re a few years off one of these algorithms writing a good novel, but I hope to demonstrate that my circa 350 generated words would be successful in facilitating the process of composition:

be as the whoo, put out and going to Ingleway effect themselves old shadows as she was like a farmers of his lake, for all or grips — that else bigs they perfectly clothes and the table and chest and under her destynets called a fingers of hanged staircase and cropping in her hand from him, “never married them my said?” know’s prode another hold of the utals of the bright silence and now he was much renderuched, his eyes. It was her natural dependent clothes, cattle that they came in loads of the remarks he was there inside him. There were she was solid drugs.

“I’m sons to see, then?’ she have no such description. The legs that somewhere to chair followed, the year disappeared curl at an entire of him frwented her in courage had approached. It was a long rose of visit. The moment, the audience on the people still the gulsion rowed because it was a travalious. But nothing in the rash.

“No, Jane. What does then they all get out him, but? Or perfect?”

“The advices?”

Of came the great as prayer. He said the aspect who, she lay on the white big remarking through the father — of the grandfather did he had seen her engoors, came garden, the irony opposition on his colling of the roof. Next parapes he had coming broken as though they fould

has a sort. Quite angry to captraita in the fact terror, and a sound and then raised the powerful knocking door crawling for a greatly keep, and is so many adventored and men. He went on. He had been her she had happened his hands on a little hand of a letter and a road that he had possibly became childish limp, her keep mind over her face went in himself voice. He came to the table, to a rashes right repairing that he fulfe, but it was soldier, to different and stuff was. The knees as it was a reason and that prone, the soul? And with grikening game. In such an inquisilled-road and commanded for a magbecross that has been deskled, tight gratulations in front standing again, very unrediction and automatiled spench and six in command, a

I don’t think I’d be alone in thinking that there’s some merit in parts of this writing. I wonder if there’s an extent to which Finnegans Wake has ‘tainted’ the corpus somewhat, because stylistically, I think that’s the closest analogue to what could be said to be going on here. Interestingly, it seems to be formulating its own puns, words like ‘unrediction,’ ‘automatiled spench’ (a tantalising meta-textual reference I think) and ‘destynets’, I think, would all be reminiscent of what you could expect to find in any given section of the Wake, but they don’t turn up in the corpus proper, at least according to a ctrl + f search. What this suggests to me is that the algorithm is plotting relationships on the level of the character, as well as phrasal units. However, I don’t recall the sci-fi model turning up paragraphs that were quite so disjointed and surreal — they didn’t make loads of sense, but they were recognisable, as grammatically coherent chunks of text. Although this could be the result of working with a partially trained model.

So, how might they feed our creative process? Here’s my attempt at making nice sentences out of the above.

— I have never been married, she said. — There’s no good to be gotten out of that sort of thing at all.

He’d use his hands to do chin-ups, pull himself up over the second staircase that hung over the landing, and he’d hang then, wriggling across the awning it created over the first set of stairs, grunting out eight to ten numbers each time he passed, his feet just missing the carpeted surface of the real stairs, the proper stairs.

Every time she walked between them she would wonder which of the two that she preferred. Not the one that she preferred, but the one that were more her, which one of these two am I, which one of these two is actually me? It was the feeling of moving between the two that she could remember, not his hands. They were just an afterthought, something cropped in in retrospect.

She can’t remember her sons either.

Her life had been a slow rise, to come to what it was. A house full of men, chairs and staircases, and she wished for it now to coil into itself, like the corners of stale newspapers.

The first thing you’ll notice about this is that it is a lot shorter. I started off by traducing the above, in as much as possible, into ‘plain words’ while remaining faithful to the n-grams I liked, like ‘bright silence’ ‘old shadows’ and ‘great as prayer’. In order to create images that play off one another, and to account for the dialogue, sentences that seemed to be doing similar things began to cluster together, so paragraphs organically started to shrink. Ultimately, once the ‘purpose’ of what I was doing started to come out, a critique of bourgeois values, memory loss, the nice phrasal units started to become spurious, and the eight or so paragraphs collapsed into the three and a half above. This is also ones of my biggest writing issues, I’ll type three full pages and after the editing process they’ll come to no more than 1.5 paragraphs, maybe?

The thematic sense of dislocation and fragmentation could be a product of the source material, but most things I write are about substance-abusing depressives with broken brains cos I’m a twenty-five year old petit-bourgeois male. There’s also a fairly pallid Enright vibe to what I’ve done with the above, I think the staircases line could come straight out of The Portable Virgin.

Maybe a more well-trained corpus could provide better prompts, but overall, if you want better results out of this for any kind of creative praxis, it’s probably better to be a good writer.

Modelling Humanities Data Blog Post #1 Deleuze, Descartes and Data to Knowledge

While dealing with the distinctions between data, knowledge and information in class, a pyramidal hierarchy was proposed, which can be seen on the left. This diagram discloses the process of making data (which have been defined as ‘facts’ which exist in the world), into information, and thereafter knowledge. These shifts from one state to another are not as neat as the diagram might suggest; it is just one interpretation giving shape to a highly dynamic and unsettled process; any movement from one of these levels to another is fraught. It is ‘a bargaining system,’ as every dataset has its limitations and aporias, not to speak of the process of interpretation or subsequent dissemination. This temporal dimension to data, its translation from a brute state is too often neglected within certain fields of study, fields in which data is more often understood as unambiguous, naturally hierarchicalised, and not open to contextualisation or debate.

This blog post aims to consider these issues within the context of a dataset obtained from The Central Statistics Office. The dataset contains information relating to the relative risk of falling into poverty based on one’s level of education between the years 2004 and 2015 inclusive. The data was analysed through use of the statistical analysis interface SPSS.

The purpose of the CSO is to compile and disseminate information relating to economic and social conditions within the state in order to give direction to the government in the formulation of policy. Therefore it was decided that the most pertinent information to be derived from the dataset would be the correlations between level of education and the likelihood of falling into poverty. The results appear below.

Correlation Between Risk of Poverty and Level of Education Achieved

Correlation Between Consistent Poverty (%) and Level of Education Received

Correlation Between Deprivation Rate (%) and Level of Education Received

Poverty Risk Based on Education Level

Deprivation Rate Based on Education Level

Consistent Poverty Rate based on Education Level

It can be seen that there is a very strong negative correlation between one’s level of education and one’s risk of exposure to poverty; the higher one ascends through the education system, the less likely it is one will fall into economic liminality. This is borne out both in the bar charts and the correlation tables, the latter of which yield p-values of .000, underlining the certainty of the finding. It should be noted that both graphing the data, and detecting correlations through use of the Spearman’s rho are elementary statistical procedures, but as the trend revealed here is consistent with more elaborate modelling of the relationship,[1] the parsimonious analysis carried out here is all that is required.

It should not be assumed that just because these graphs are informative that it is impossible to garner information from data in any other way. Even in its primary state, as it appears on the website, one could obtain information from a dataset through qualitative means. It is unlikely that this information will be as coherent as that which that can be gleaned from even the most basic graph, but it is important to emphasise the fact that the border that separates data from information is fluid.

It is unlikely to be a novel finding that those who have a third level education have higher incomes than those who do not; there is a robust body of research detailing the many benefits of attending university. [2] Therefore, can it be said that the visualisation of the dataset above has contributed to knowledge? One would answer this question relative to one’s initial research question, and how the information complicates or advances it. If the causal relationship between exposure to poverty and level of education has been confirmed, and a government agency makes the recommendation that further investment in educational support programmes are necessary, it is somewhere in this process that the boundary separating information from knowledge has been crossed.

The above diagram actualises the temporal nature of data to a greater extent than the pyramid, but in doing so it perpetuates a linearisation of the process, a line along which René Descartes’ notion of thought could be said to align. Descartes understood thought as a positive function which tends towards the good and toward truth. This ‘good sense’, allows us to ‘judge correctly and to distinguish the true from the false’.[3] Gilles Deleuze believes Descartes instantiates a model of thought which is oppressive, and which perceives thinking relative to external needs and values rather than in its actuality: ‘It cannot be regarded as fact that thinking is the natural exercise of a faculty, and that this faculty is possessed of a good nature and a good will.’[4]

In Deleuze’s conception, thought takes on a sensual disposition, reversing the Cartesian notion of mental inquiry beginning from a state of disinterestedness in order to arrive at a moment at which one recognises ‘rightness’. Deleuze argues that there is no such breakthrough moment or established methodology to thought, and argues for regarding it as more invasive, or unwelcome, a point of encounter when ‘something in the world forces us to think.’[5]

Rather than taking the neat, schematic movement from capturing data to modelling to interpreting for granted, Deleuze is engaged by these moments of crisis, points just before or just after the field of our understanding is qualitatively transformed into something different:

How else can one write but of those things which one doesn’t know, or know badly?…We write only at the frontiers of our knowledge, at the border which separates our knowledge from our ignorance and transforms one into the other.[6]

Deleuze’s comments have direct bearing upon our understanding of data, and how they should be understood within the context of the wider questions we ask of them. Deleuze argues that, ‘problems must be considered not as ‘givens’ (data) but as ideal ‘objecticities’ possessing their own sufficiency and implying acts of constitution and investment in their respective symbolic fields.’[7] While it is possible that Deleuze would risk overstating the case, were we to apply his theories to this dataset, it is nonetheless crucial to recall that data, and the methodologies we use to unpack and present them participate in wider economies of significance, ones with indeterminate horizons.


[1] Department for Business, Education and Skills, ‘BIS Research Paper №146: The Benefits of Higher Education and Participation for Individuals and Society: Key Findings and Reports’, (Department for Business, Education and Skills: 2013) https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/254101/bis-13-1268-benefits-of-higher-education-participation-the-quadrants.pdf

[2] OECD, Education Indicators in Focus, (OECD: 2012) https://www.oecd.org/education/skills-beyond-school/Education%20Indicators%20in%20Focus%207.pdf

[3] Descartes, René, Discourse on the Method of Rightly Conducting the Reason, and Seeking Truth in the Sciences (Gutenberg: 2008), http://www.gutenberg.org/files/59/59-h/59-h.htm

[4] Deleuze, Gilles, Difference and Repetition (Bloomsbury Academic: 2016), p.175

[5] Ibid.

[6] Ibid, p. xviii

[7] Ibid, p.207


Deleuze, Gilles, Difference and Repetition (Bloomsbury Academic: 2016), p.175

Department for Business, Education and Skills, ‘BIS Research Paper №146: The Benefits of Higher Education and Participation for Individuals and Society: Key Findings and Reports’, (Department for Business, Education and Skills: 2013) https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/254101/bis-13-1268-benefits-of-higher-education-participation-the-quadrants.pdf

Descartes, René, Discourse on the Method of Rightly Conducting the Reason, and Seeking Truth in the Sciences (Gutenberg: 2008), http://www.gutenberg.org/files/59/59-h/59-h.htm

OECD, Education Indicators in Focus, (OECD: 2012) https://www.oecd.org/education/skills-beyond-school/Education%20Indicators%20in%20Focus%207.pdf

Statistical Correlations in avant-garde prose writing

The question that this blog post sets itself is: What differences and similarities can be detected in modernist and contemporary authors on the basis of three stylistic variables; hapax, unique and ambiguity, and how are these stylistic variables related to one another?

I: The Data

The data to be analysed in this project were derived from an analysis of twenty-one corpora of avant-garde literary prose through use of the open-source programming language R. The complete works of the authors James Joyce, Virginia Woolf, Gertrude Stein, Sara Baume, Anne Enright, Will Self, F. Scott FitzGerald, Eimear McBride, Ernest Hemingway, Jorge Luis Borges, Joseph Conrad, Ford Madox Ford, Franz Kafka, Katherine Mansfield, Marcel Proust, Elizabeth Bowen, Samuel Beckett, Flann O’Brien, Djuna Barnes, William Faulkner & D.H. Lawrence were used.

Seventeen of these writers were active between the years 1895 and 1968, a period of time associated with a genre of writing referred to as ‘modernist’ within the field of literary criticism. The remaining four remain alive, and have novels published as early as 1991, and as late as 2016. These novelists are known for their identification as latter-day modernists, and perceive their novels as re-engaging with the modernist aesthetic in a significant way.

I.II Uniqueness

The unique variable is a generally accepted measurement used within digital literary criticism to quantify the ‘richness’ of a particular text’s vocabulary. The formula for uniqueness is obtained by dividing the number of distinct word types in a text by the total number of words. For example, if a novel contained 20000 word types, but 100000 total words, the formula for obtaining this text’s uniqueness would be as follows:

20000/100000 = Uniqueness is equal to 0.2

I.III Ambiguity

Ambiguity is a measure used to calculate the approximate obscurity of a text, or the extent to which it is composed of indefinite pronouns. The indefinite pronouns quantified in this study are as follows, ‘another’, ‘anybody’, ‘anyone’, ‘anything’, ‘each’, ‘either’, ‘enough’, ‘everybody’, ‘everyone’, ‘everything’, ‘little’, ‘much’, ‘neither’, ‘nobody’, ‘no one’, ‘nothing’, ‘one’, ‘other’, ‘somebody’, ‘someone’, ‘something’, ‘both’, ‘few’, ‘everywhere’, ‘somewhere’, ‘nowhere’, ‘anywhere’, ‘many’, ‘others’, ‘all’, ‘any’, ‘more’, ‘most’, ‘none’, ‘some’, ‘such’. The formula for ambiguity is:

number of indefinite pronouns / number of total words

I.IV Hapax

Finally, the hapax variable calculates the density of hapax legomena, words which appear only once in a particular author’s oeuvre. The formula for this variable is:

number of hapax legomena / number of total words

a bar chart giving an overview of the data

II: Data Overview

Even before analysing the data in great depth, the fact that these variables are interrelated with one another stands to a logical analysis. Hapax and unique are best understood as an indication of a text’s heterogeneity, as if a text is hapax-rich, the score for uniqueness will be similarly elevated. Ambiguity, as it is a set of pre-defined words, can be considered a measure of a text’s homogeneity, and if the occurrences of these commonplace words are increasing, hapax and uniqueness will be negatively effected. The aim of this study will be to first determine how these measures vary according to the time frame in which the different texts were written, i.e. across modern and contemporary corpora, which correlations between stylistic variables exist, and which of the three is most subject to the fluctuations of another.

more overviews for each variable

IV.I: The Three Groups Hypothesis

A number of things are clear from these representations of the data. The first finding is that the authors fall into approximately three distinct groups. The first is the base- level of early twentieth-century modernist authors, who are all relatively undifferentiated. These are Ernest Hemingway, Virginia Woolf, William Faulkner, Elizabeth Bowen, Marcel Proust, F. Scott Fitzgerald, D.H. Lawrence, Joseph Conrad and Ford Madox Ford. They are all below the mean for the hapax and unique variables.

boxplot of outliers for the unique hapax variable

The second group reach into more extreme values for unique and hapax. These are Djuna Barnes, Jorge Luis Borges, Franz Kafka, Flann O’Brien, James Joyce, Eimear McBride and Sara Baume. Three of these authors are even outliers for the hapax variable, which can be seen in the box plot.

Joyce’s position as an extreme outlier in this context is probably due to his novel Finnegans Wake (1939), which was written in an amalgam of English, French, Irish, Italian and Norwegian. It’s no surprise then, that Joyce’s value for hapax is so high. The following quotation may be sufficient to give an indication of how eccentric the language of the novel is:

La la la lach! Hillary rillarry gibbous grist to our millery! A pushpull, qq: quiescence, pp: with extravent intervulve coupling. The savest lauf in the world. Paradoxmutose caring, but here in a present booth of Ballaclay, Barthalamou, where their dutchuncler mynhosts and serves them dram well right for a boors’ interior (homereek van hohmryk) that salve that selver is to screen its auntey and has ringround as worldwise eve her sins (pip, pip, pip)

Though Borges’ and Barnes’ prose may not be as far removed from modern English as Finnegans Wake, both of these authors are known for their highly idiosyncratic use of language; Borges for his use of obscure terms derived from archaic sources, and Barnes for reversing normative grammatical and syntactic structures in unique ways.

The third and final group may be thought of as an intermediary between these two extremes, and these are Katherine Mansfield, Samuel Beckett, Will Self and Anne Enright. These authors share characteristics of both groups, in that the values for ambiguity remain stable, but their uniqueness and hapax counts are far more pronounced than the first group, but not to the extent that they reach the values of the second group.

boxplot displaying stein as an extreme outlier for ambiguity

Gertrude Stein is the only author who’s stylistic profile doesn’t quite fit into any of the three groups. She is perhaps best thought of as most closely analogous to the first group of early twentieth century modernists, but her extreme value for ambiguity should be sufficient to distinguish her in this regard.

The value for ambiguity remains fairly stable throughout the dataset, the standard deviation is 0.03, but if Stein’s values are removed from the dataset, the standard deviation narrows from 0.03 to 0.01.

Two disclaimers need to be made about this general account from the descriptive statistics and graphs. The first is that there is a fundamental issue with making such a schematic account of these texts. The grouping approach that this project has taken thus far is insufficiently nuanced as it could probably be argued that McBride could just as easily fit into the third group as the second. Therefore, the stylistic variables do not adequately distinguish modern and contemporary corpora from one another.

IV.II Word Count

word count for the most prolific authors

It should not escape our attention that those authors who score lowest for each variable and that the first group of early twentieth-century author are the most prolific. The correlation between word count and the stylistic variables was therefore constructed.

Pearson correlation for word count and stylistic variables

Both the Pearson correlation and Spearman’s rho suggest that word count is highly negatively correlated with hapax and unique (as word count increases, hapax and unique decreases and vice versa), but not with ambiguity.

Spearman’s rho for word count and stylistic variables

The fact that the Spearman’s rho scores significantly higher than the Pearson suggests that the relationship between the two are non-linear. This can be seen in the scatter plot.

scatter plot showing the relationship between word count and uniqueness

In the case of both variables, the correlation is obviously negative, but the data points fall in a non-linear way, suggesting that the Spearman’s rho is the better measure for calculating the relationship. In both cases it would seem that Joyce is the outlier, and most likely to be the author responsible for distorting the correlation.

scatter plot displaying the relationship between word count and hapax density
Pearson correlations for word count and each stylistic variable

SPSS flags the correlation between hapax and unique as being significant, as this is clearly the most noteworthy relationship between the three stylistic variables. The Spearman’s rho exceeded the Spearman correlation by a marginal amount, and it was therefore decided that the relationship was non-linear, which is confirmed by the scatter plot below:

Spearman’s rho correlation for word count and stylistic variables

The stylistic variables of unique and hapax are therefore highlycorrelated.

VI: Conclusion

As was said already, the notion that stylistic variables are correlated stands to reason. However, it was not until the correlation tests were carried out that the extent to which uniqueness and hapax are determined by one another was made clear.

The biggest issue with this study is the issue that is still present within digital comparative analyses in literature generally; our apparent incapacity to compare texts of differing lengths. Attempts have been made elsewhere to account for the huge difference that a text’s length clearly makes to measures of its vocabulary, such as vectorised analyses that take measurements in 1000 word windows, but none have yet been wholly successful in accounting for this difference. This study is therefore one among many which presents its results with some clarifiers, considering how corpora of similar lengths clustered together with one another to the extent that they did. The only author that violated this trend was Joyce, who, despite a lengthy corpus of 265500 words, has the highest values for hapax and uniqueness, which marks his corpus out as idiosyncratic. Joyce’s style is therefore the only of the twenty-one authors that we can say has a writing style that can be meaningfully distinguished from the others on the basis of the stylistic variables, because he so egregiously reverses the trend.

But we hardly needed an analysis of this kind to say Joyce writes differently from most authors, did we.

A (Proper) Statistical analysis of the prose works of Samuel Beckett


Content warning: If you want to get to the fun parts, the results of an analysis of Beckett’s use of language, skip to sections VII and VIII. Everything before that is navel-gazing methodology stuff.

If you want to know how I carried out my analysis, and utilise my code for your own purposes, here’s a link to my R code on my blog, with step-by-step instructions, because not enough places on the internet include that.

I: Things Wrong with my Dissertation’s Methodology

For my masters, I wrote a 20000 word dissertation, which took as its subject, an empirical analysis of the works of Samuel Beckett. I had a corpus of his entire works with the exception of his first novel Dream of Fair to Middling Women, which is a forgivable lapse, because he ended up cannibalising it for his collection of short stories, More Pricks than Kicks.

Quantitative literary analysis is generally carried out in one of two ways, through either one of the open-source programming languages Python or R. The former you’ve more likely to have heard of, being one of the few languages designed with usability in mind. The latter, R, would be more familiar to specialists, or people who work in the social sciences, as it is more obtuse than Python, doesn’t have many language cousins and has a very unfriendly learning curve. But I am attracted to difficulty, so I am using it for my PhD analysis.

I had about four months to carry out my analysis, so the idea of taking on a programming language in a self-directed learning environment was not feasible, particularly since I wanted to make a good go at the extensive body of secondary literature written on Beckett. I therefore made use of a corpus analysis tool called Voyant. This was a couple of years ago, so this was before its beta release, when it got all tricked out with some qualitative tools and a shiny new interface, which would have been helpful. Ah well. It can be run out of any browser, if you feel like giving it a look.

My analysis was also chronological, in that it looked at changes in Beckett’s use of language over time, with a view to proving the hypothesis that he used a less wide vocabulary as his career continued, in pursuit of his famed aesthetic of nothingness or deprivation. As I wanted to chart developments in his prose over time, I dated the composition of each text, and built a corpus for each year, from 1930–1987, excluding of course, years in which he just wrote drama, poetry, which wouldn’t be helpful to quantify in conjunction with one another. Which didn’t stop me doing so for my masters analysis. It was a disaster.

II: Uniqueness

Uniqueness, the measurement used to quantify the general spread of Beckett’s vocabulary, was obtained by the generally accepted formula below:

unique word tokens / total words

There is a problem with this measurement, in that it takes no account of a text’s relative length. As a text gets longer, the likelihood of each word being used approaches 1. Therefore, a text gets less unique as it gets bigger. I have the correlations to prove it:

Screen Shot 2016-11-03 at 12.18.03.png

There have been various solutions proposed to this quandary, which stymies our comparative analyses, somewhat. One among them is the use of vectorised measurements, which plot the text’s declining uniqueness against its word count, so we see a more impressionistic graph, such as this one, which should allow us to compare the word counts for James Joyce’s novels, A Portrait of the Artist as a Young Man and his short story collection, Dubliners.

Screen Shot 2016-11-03 at 13.28.18.png

All well and good for two or maybe even five texts, but one can see how, with large scale corpora, this sort of thing can get very incoherent very quickly. Furthermore, if one was to examine the numbers on the y-axis, one can see that the differences here are tiny. This is another idiosyncrasy of stylostatistical methods; because of the way syntax works, the margins of difference wouldn’t be regarded as significant by most statisticians. These issues relating to the measurement are exacerbated by the fact that ‘particles,’ the atomic structures of literary speech, (it, is, the, a, an, and, said, etc.) make up most of a text. In pursuit of greater statistical significance for their papers, digital literary critics remove these particles from their texts, which is another unforgivable that we do anyway. I did not, because I was concerned that I was complicit in the neoliberalisation of higher education. I also wrote a 4000 word chapter that outlined why what I was doing was awful.

IV: Ambiguity

The formula for ambiguity was arrived at by the following formula:

number of indefinite pronouns/total word count

I derived this measurement from Dr. Ian Lancashire’s study of the works of Agatha Christie, and counted Beckett’s use of a set of indefinite pronouns, ‘everyone,’ ‘everybody,’ ‘everywhere,’ ‘everything,’ ‘someone,’ ‘somebody,’ ‘somewhere,’ ‘something,’ ‘anyone,’ ‘anybody,’ ‘anywhere,’ ‘anything,’ ‘no one,’ ‘nobody,’ ‘nowhere,’ and ‘nothing.’ Those of you who know that there are more indefinite pronouns than just these, you are correct, I had found an incomplete list of indefinite pronouns, and I assumed that that was all. This is just one of the many things wrong with my study. My theory was that there were to be correlations to be detected in Beckett’s decreasing vocabulary, and increasing deployment of indefinite pronouns, relative to the total word count. I called the vocabulary measure ‘uniqueness,’ and the indefinite pronouns measure I called ‘ambiguity.’ This in tenuous I know, indefinite pronouns advance information as they elide the provision of information. It is, like so much else in the quantitative analysis of literature, totally unforgivable, yet we do it anyway.

V: Hapax Richness

I initially wanted to take into account another phenomenon known as the hapax score, which charts occurrences of words that appear only once in a text or corpus. The formula to obtain it would be the following:

number of words that appear once/total word count

I believe that the hapax count would be of significance to a Beckett analysis because of the points at which his normally incompetent narrators have sudden bursts of loquaciousness, like when Molloy says something like ‘digital emunction and the peripatetic piss,’ before lapsing back into his ‘normal’ tone of voice. Once again, because I was often working with a pen and paper, this became impossible, but now that I know how to code, I plan to go over my masters analysis, and do it properly. The hapax score will form a part of this new analysis.

VI: Code & Software

A much more accurate way of analysing vocabulary, for the purposes of comparative analysis when your texts are of different lengths, therefore, would be to randomly sample it. Obviously not very easy when you’re working with a corpus analysis tool online, but far more straightforward when working through a programming language. A formula for representative sampling was found, and integrated into the code. My script is essentially a series of nested loops and if/else statements, that randomly and sequentially sample a text, calculate the uniqueness, indefiniteness and hapax density ten times, store the results in a variable, and then calculate the mean value for each by dividing the result by ten, the number of times that the first loop runs. I inputted each value into the statistical analysis program SPSS, because it makes pretty graphs with less effort than R requires.

VII: Results

I used SPSS’ box plot function first to identify any outliers for uniqueness, hapax density and ambiguity. 1981 was the only year which scored particularly high for relative usage of indefinite pronouns.


It should be said that this measure too, is correlated to the length of the text, which only stands to reason; as a text gets longer the relative incidence of a particular set of words will decrease. Therefore, as the only texts Beckett wrote this year, ‘The Way’ and ‘Ceiling,’ both add up to about 582 words (the fifth lowest year for prose output in his life), one would expect indefiniteness to be somewhat higher in comparison to other years. However, this doesn’t wholly account for its status as an outlier value. Towards the end of his life Beckett wrote increasingly short prose pieces. Comment C’est (How It Is) was his last novel, and was written almost thirty years before he died. This probably has a lot to do with his concentration on writing and directing his plays, but in his letters he attributed it to a failure to progress beyond the third novel in his so-called trilogy of Molloy, Malone meurt (Malone Dies) and L’innomable (The Unnamable). It is in the year 1950, the year in which L’inno was completed, that Beckett began writing the Textes pour rien (Texts for Nothing), scrappy, disjointed pieces, many of which seem to be taking up from where L’inno left off, similarly the Fizzlesand the Faux Départs. ‘The Way,’ I think, is an outgrowth of a later phase in Beckett’s prose writing, which dispenses the peripatetic loquaciousness and the understated lyricism of the trilogy and replaces it with a more brute and staccato syntax, one which is often dependent on the repetition of monosyllables:

No knowledge of where gone from. Nor of how. Nor of whom. None of whence come to. Partly to. Nor of how. Nor of whom. None of anything. Save dimly of having come to. Partly to. With dread of being again. Partly again. Somewhere again. Somehow again. Someone again.

Note also the prevalence of particle words, that will have been stripped out for the analysis, and the ways in which words with a ‘some’ prefix are repeated as a sort of refrain. This essential structure persists in the work, or at least the artefact of the work that the code produces, and hence of it, the outlier that it is.

Screen Shot 2016-11-03 at 12.55.13.png

From plotting all the values together at once, we can see that uniqueness is partially dependent on hapax density; the words that appear only once in a particular corpus would be important in driving up the score for uniqueness. While there could said to be a case for the hypothesis that Beckett’s texts get less unique, more ambiguous up until 1944, when he completed his novel Watt, and if we’re feeling particularly risky, up until 1960 when Comment C’est was completed, it would be wholly disingenuous to advance it beyond this point, when his style becomes far too erratic to categorise definitively. Comment C’est is Beckett’s most uncompromising prose work. It has no punctuation, no capitalisation, and narrates the story of two characters, in a kind of love, who communicate with one another by banging kitchen implements off another:

as it comes bits and scraps all sorts not so many and to conclude happy end cut thrust DO YOU LOVE ME no or nails armpit and little song to conclude happy end of part two leaving only part three and last the day comes I come to the day Bom comes YOU BOM me Bom ME BOM you Bom we Bom

VIII: Conclusion

I would love to say that the general tone is what my model is being attentive to, which is why it identified Watt and How It Is as nadirs in Beckett’s career but I think their presence on the chart is more a product of their relative length, as novels, versus the shorter pieces which he moved towards in his later career. Clearly, Beckett’s decision to write shorter texts, make this means of summing up his oeuvre in general, insufficient. Whatever changes Beckett made to his aesthetic over time, we might not need to have such complicated graphs to map, and I could have just used a word processor to find it — length. Bom and Pim aside, for whatever reason after having written L’inno none of Beckett’s creatures presented themselves to him in novelistic form again. The partiality of vision and modal tone which pervades the post-L’inno works demonstrates, I think far more effectively what is was that Beckett was ‘pitching’ for, a new conceptual aspect to his prose, which re-emphasised its bibliographic aspects, the most fundamental of which was their brevity, or the appearance of an incompleteness, by virtue of being honed to sometimes less than five hundred words.

The quantification of differing categories of words seems like a radical, and the most fun, thing to quantify in the analysis of literary texts, as the words are what we came for, but the problem is similar to one that overtakes one who attempts to read a literary text word by word by word, and unpack its significance as one goes: overdetermination. Words are kaleidoscopic, and the longer you look at them, the more threatening their darkbloom becomes, the more they swallow, excrete, the more alive they are, all round. Which is fine. Letting new things into your life is what it should be about, until their attendant drawbacks become clear, and you start to become ambivalent about all the fat and living things you have in your head. You start to wish you read poems instead, rather than novels, which make you go mad, and worse, start to write them. The point is words breed words, and their connections are too easily traced by computer. There’s something else about knowing that their exact correlations to a decimal point. They seem so obvious now.

Walkthrough of Textual Analysis Through R

The attached PDF (used because not even .txt files, let alone .r ones are supported by wordpress) is a 100-some line code in RStudio that I’ve used for some basic forays into textual analysis.

It is essentially useless, and quantifies three textual phenomena: richness of vocabulary, density of indefinite pronouns and density of hapax legomena, or words that appear only once. All measures are obtained from sequential samples of words, the size of which is based on the size of the text that is ‘fed’ into the code.

The measures are essentially useless; all variables are essentially contingent on one another, that is, if uniqueness goes up, indefinite pronoun density would have to go down, and hapax density would go up, though not to the same extent that indefinite would decrease, since these last two are arbitrary groupings of words, of course their increases would be to uniqueness’ detriment. Mostly I just needed to get some code up and running for a statistics project.

Comments are included to give a sense of what each line is doing, because not enough people using R for literary analysis do that.

Thanks are due to Matthew L. Jockers for his book, Text Analysis with R for Students of Literature, which I found literally indispensable.