DHQ: Digital Humanities Quarterly
2007
Volume 1 Number 1
Volume 1 Number 1
Reading Potential: The Oulipo and the Meaning of Algorithms
Abstract
Recent efforts to reconceptualize text analysis with computers in order to broaden the appeal of humanities computing have invoked the example of the Oulipo. Although there are similarities between the activities of the Oulipo and the new approach to computer-assisted literary analysis, the development of tools for the express purpose of encouraging scholars to play with texts does not follow the model of Oulipian research into potentialities. For the Oulipo, potential text analysis is less a question of interpreting literature than of supplying algorithms for the good use one can make of reading. Producing exemplary interpretations with algorithms is a secondary consideration. Oulipian constraints are better understood as toys with no intended purpose rather than as tools we use with some objective in mind. The procedures for making sense of texts provide for their own interpretation: they are not only instruments for discovering meaning but also reflections on making meaning.
Researchers in the humanities who focus their efforts on the use of computer technology
to engage texts have wrestled with the relevance of their work. Within the field of
humanities computing, scholars have used information technology to analyze single texts
as well as large corpora in search of patterns that would be difficult to detect without
the use of machines. Despite the ability of computers to organize and process massive
amounts of textual data, the broader community of literary scholars has not readily
accepted the potential of digital technology in humanities research. In 1993, Mark Olsen
argued that scholars using computers to analyze texts tended to focus on quantifying a
literary effect instead of exploring how texts are meaningful in broader historical and
cultural contexts [Olsen 1993]. Recent efforts to reconceptualize text
analysis with computers have emphasized hypothesis testing and the search for elusive
patterns that may provide insight into interpreting texts. In order to broaden the
appeal of this new direction in humanities computing, advocates have invoked the example
of the Oulipo, a group of writers in France that invent “potential” ways to create
literature using rigorous formal constraints. The idea that computers can lend
themselves to formal methods of subjective textual study (thereby assuaging concerns
that computers will make reading literature a soulless process of crunching numbers) is
expressed forcefully by Jerome McGann in his book Radiant
Textuality. For McGann, the Oulipo set an example of what can be done with
combinatorial methods to realize Alfred Jarry's program of pataphysics as a “science of exceptions”
[McGann 2001a, 222]. Rejecting the practice of using computers as tools for objective, empirical
research, Stephen Ramsay envisions an algorithmic criticism that transforms texts “for the purpose of releasing what the
Oulipians would call their ‘potentialities’
”
[Ramsay 2003, 172]. Stéfan Sinclair has developed HyperPo as a web-based tool for helping scholars
read and play with texts using procedures inspired by the Oulipo [Sinclair 2003]. The idea of playing with texts using computers is pursued
further by Geoffrey Rockwell who calls for the creation of web-based playpens where
scholars can experiment with tools and discover new ways to formulate and test
hypotheses about what texts mean [Rockwell 2003].
Although there are similarities between the activities of the Oulipo and the new
approach to computer-assisted literary analysis, the development of tools for the
express purpose of encouraging scholars to play with texts does not follow the model of
Oulipian research into potentialities. For the Oulipo, the invention of procedures for
playing with texts is its own end, an intellectual activity that invites application but
does not require adoption by others as an indication of success. According to Raymond
Queneau, one of the founding members of the Oulipo, “The word ‘potential’ concerns the very
nature of literature; that is, fundamentally it's less a question of literature
strictly speaking than of supplying forms for the good use one can make of
literature. We call potential literature the search for new forms and structures
that may be used by writers in any way they see fit”
[Motte 1986, 38]. Queneau makes it clear that what the Oulipo does relates to but does not
constitute literary creation. Writing is a derivative activity: the Oulipo pursue what
we might call speculative or theoretical literature and leave the application of the
constraints to practitioners who may (or may not) find their procedures useful.
According to François Le Lionnais, another founding member, a method for writing
literature need not produce an actual text: “method is sufficient in and of itself. There
are methods without textual examples. An example is an additional pleasure for the
author and the reader”
[Bens 1980, 88].
The Oulipo did not articulate a clear statement explaining potential methods for reading
literature, but we can extrapolate a definition from how they described their efforts to
invent methods for writing literature. Potential text analysis is less a question
of interpreting literature than of supplying algorithms for the good use one can make
of reading. Producing exemplary interpretations with algorithms is a secondary
consideration. It follows that the interpretation of texts using a computer
should not be in and of itself the objective of potential text analysis. The objective
should be the invention of algorithms that scholars may (or may not) use, according to
their own interests. The potentiality of computers as tools for text analysis implies
that scholars engaged in the derivative activity of interpreting literature may not find
such methods useful.
By inventing procedures for generating texts, the Oulipo separated the formal aspects of
writing from its content so that procedures for making texts could be carried out
independently of those who invent the procedures. As the Oulipo declared in a
presentation of its work to the Collège de 'Pataphysique, this is a new era in the
history of literature: “Thus, the time of created
creations, which was that of the literary works we know, should cede to
the era of creating creations, capable of developing from themselves
and beyond themselves, in a manner at once predictable and inexhaustibly
unforeseen”
[Motte 1986, 48–49]. The transition from created creations to creating
creations divides the traditional author function into what Christelle
Reggiani identifies as the biphasic Oulipian functions of the inventor and the poet [Reggiani 1999, 110]. The first phase involves an inventor who devises
constraints that will guide the production of a text. The inventor does not worry about
what the constraints will produce: he seeks consistency and robustness in formal
procedures. During the second phase, a poet applies the constraints in a particular
instance and produces a text. Clearly the role of the inventor is privileged over the
poet. Whereas the poet can only follow the rules of literary form and assemble a finite
number of texts, the inventor explores potential forms which can generate innumerable
texts.
When the Oulipo formed in 1960, one of the first things they discussed was using
computers to read and write literature. They communicated regularly with Dmitri
Starynkevitch, a computer programmer who helped develop the IBM SEA CAB 500 computer.
The relatively small size and low cost of the SEA CAB 500 along with its high-level
programming language PAF (Programmation Automatique des Formules) provided the Oulipo
with a precursor to the personal computer [Starynkevitch 1990].
Starynkevitch used the machine to create an imaginary telephone directory composed of
realistic names and numbers generated by his computer:
The algorithms Starynkevitch used were based mainly on random number
generators. Given names and street names were selected from a predetermined list.
Surnames were composed from sets of letter sequences that alternated between open and
closed syllables [Bens 1980, 162–163]. The Oulipo was impressed with
the mock phone book but Queneau did not believe the computer application had “potential”. Le Lionnais found the phone
book interesting because it was not particularly interesting: it was neither bizarre nor
funny, and it looked like a real phone book. What worried the Oulipo was the aleatory
nature of computer-assisted artistic creation: they sought to avoid chance and
automatisms over which the computer user had no control [Bens 1980, 157–8].
- Tab Philippe, 14, rue de La Machine normande
- Dubit Anatole, 20, av. du Moine Romain
- Pouguinf Jules, 45, rue de la Maison
- Herebier Adolphe, 38, rue des Maisons Jolies
- Lir Yves, 64, rue Saint-Pierre
- Lorbont Edouard, 21, av. du Buisson Gai
- Sech André, 18, rue des Montagnes riveraines
- Dreber Gilbert, 5, rue Jules Marcel
- Micier Michel, 54, rue Saint Augustin
- Debate Robert, 25, rue des Montagnes
- Locrobelier Adolphe, 18, av. des Gares étroites
- Rexer Augustin, 1, rue de la Tour blonde
- Quimier Anatole, 20, rue du Buisson galant
Example 1.
Computer-generated names and addresses from Starynkevitch's telephone
directoryIn 1981 the Oulipo published Atlas de littérature potential
where they described some of the computer applications they had devised for reading
literature. Their early experiments included machine-assisted readings of Queneau's
Cent mille milliards de poèmes. In this deceptively
small book, Queneau had composed ten sonnets in such a way that the reader could select
the first line of any sonnet, the second line of any sonnet, etc., and generate one of
1014 possible sonnets. The book itself contains the
mechanism for generating poems: each line is printed on a strip of paper, and the reader
can select strips from the original sonnets to generate a potential sonnet [Queneau 1961]. Dimitri Starynkevitch had programmed his SEA CAB 500 machine
to compose sonnets from Queneau's Cent mille milliards de
poèmes. In 1975 the Atelier de Recherches et Techniques Avancées, or ARTA,
wrote a computer program that produced instantiations of the Cent
mille milliards de poèmes as a function of a user's name and the time it took
him or her to type it. It is not difficult to simulate ARTA's computer program to
produce poems from Queneau's original text by counting the number of seconds it takes a
user to type his or her name and using that information to calculate a “magic
number”:
Each digit in the magic number corresponds to a verse from one of the original
ten sonnets. The program's algorithm provides a certain degree of interaction between
the user and the machine, and the results of running the program are theoretically
reproducible if a user types the same name in the same amount of time. The algorithm
therefore has potential, but only insofar that it accelerates the production of poems.
It may be easier and more entertaining to generate poems automatically without relying
on user input, and there are several web sites on the Internet that do this. One could
even use a random number function:
Such a program has no potential in the Oulipian sense because random numbers
produce aleatory effects. The original algorithm preserves an active role for the user,
even if that role requires the minimal engagement of typing one's name in order to
sustain the creative process.
Figure 1.
A simulation of ARTA's program for generating poems from Queneau's Cent Mille Milliards de Poèmes. In English (translation
by Beverly Charles Rowe at http://www.bevrowe.info/Poems/QueneauRandom.htm; the reader can select
specific lines from the original 10 sonnets to reproduce any of the
100,000,000,000,000 potential poems) it reads as follows: The pampas king betrays his devoteesand decks the bulls that roam the paramothe native driver's waiting in the breezethe re in in [sic] all his songs came out as dohYou'll view so plain a plain with trembling kneesas castles blaze and palaces burn lowfor death casts piles of shit on pedigreesit's scary both for hick and aristoThe impassioned poet isn't polyglotone language in his brain that's all he's gotshame gives the colonel's brow a greasy sheenAll's sold the prawns the lobsters our whole stocksay sorry that no whales came back to dockunless the bell is silent and serene
Figure 2.
A program for generating poems from Queneau's Cent Mille
Milliards de Poèmes using a random number function. In English
(translation by Beverly Charles Rowe, http://www.bevrowe.info/Poems/QueneauRandom.htm) it reads as follows:
The Parthenon horse looks nervous on the friezebeing twinned is better far than single-othe chosen fruit is hued a bright ceriseall during Lent one fruit's the ratioThe Papuan sucks his friend's apophysesas castles blaze and palaces burn lowthe flanks protected by chevaux de friseit's scary both for hick and aristoThe clever students may have lost the plotto cease from scratching parchment he cannotthe motorway laps up leaked gasolineThose transalpine relations interlock?you cannot number off each ploch and pocktrue twinship blames whatever's in the gene
Paul Braffort and Jacques Roubaud, two Oulipians with backgrounds in mathematics and
computer science, formed the Atelier de Littérature Assistée par la Mathématique et les
Ordinateurs (ALAMO) in 1980 to explore computer-assisted writing. Following the model of
Queneau's Cent mille milliards de poèmes, the ALAMO wrote
computer programs to produce texts according to the rules of various genres, such as
poems and aphorisms. Braffort explained that combinatorial methods for generating texts
with computers fall into two categories. The first category, applicational methods,
involves templates for arranging words according to their grammatical function. One
particularly amusing application generates what the ALAMO calls “Rimbaudelaires”, poems based on the structure of
Rimbaud's poem “Le Dormeur du Val” and composed of vocabulary
from Baudelaire's works:
Another example is Marcel Bénabou's method for generating aphorisms [Bénabou 1980]. Braffort developed a program that operationalized Bénabou's
algorithm by abstracting the structures common to adages and substituting new terms into
the structures:
The potential of these computer programs resides in the way fragments of words
and verses are recombined according to a set of well-defined rules. Poetic forms can
thus be understood as algorithms for creating meaning with language.[1] The ALAMO devised ways to formalize
poetics in order for a computer to generate structured texts which may or may not make
sense. The actual poems produced by the programs are derivatives of the way computers
can be harnessed to explore language. Reading these computer-generated texts can be
amusing because of unexpected or incongruous combinations of words that oddly make
sense. Despite their uncanny effects, however, texts produced through applicational
methods still bear the mark of the inventor who not only determines the templates into
which syntagma are inserted but also the stock of words and phrases from which the
computer program draws.
Figure 3.
Sample Rimbaudelaire poem from the ALAMO's web site (http://alamo.mshparisnord.net/rialt/index.html). Here is a translation
(mine): There is a countryside king where a shadow flower rollsSimply hanging in the shadows pâtésOf love; where the regret of dark memoryDrinks: it is a former happiness that shines sideways.A noble demon, hatred dark and size black,His tongue bathing in the ginger-white ravine,Rains; he leans in the shade, under the lustrous fabric,Noble in his cold dale where the chest speaks falsely.The heavens in desires, it rains. This frail birdWould slide a wary shark, he flees the hail:Medallion, love him tenderly: he is great.The sweet liquors do not make his nostril stomp;It rains in the bell, the sea on his chest,Wary. He has noble skies in the slow gladiolus.
Figure 4.
Sample aphorisms from the ALAMO's web site (http://alamo.mshparisnord.net/rialt/index.html). Here is a translation
(mine): There is no rhythm where there is no form.Myth is the final sanctuary of the mind.One must not let go of the earth for confusion.There is no space where there is no laughter.One cannot serve two masters at once: obstacle and despair.One must avoid reducing war to slavery.Despair without form is only memory of glory.If you want sensuality, prepare for laughter.If happiness leads to time, it's because happiness is only ignorance.Grace is the distance man places between sin and feeling.Freedom is the gift of youth, oblivion the gift of age.Oblivion gives truth the taste of pleasure and sensuality takes it away.It is easier to tolerate pleasure than sin.What good is war before slavery?
The second category, implicational methods, involves further abstraction of structures.
Instead of creating templates for arranging words according to predetermined syntactic
and semantic categories, the inventor devises rules for making templates. According to
Braffort, “implicational methods take a further step
[in the direction of invention]. The very logic of the text is controlled by the
program: a global syntagma comes into play and becomes manifest as a supervisor of
local syntagmas”
[Braffort 1984, 18]. The logic of implicational methods relies on recursion in programs that generate
texts, allowing for systematic processing of linguistic elements below the level of the
“master” program. With recursion, computer programs can continuously refine
their output in ways the inventor would not be able to easily predict. Implicational
methods provide computers with a small measure of independence. Braffort and the other
members of the ALAMO developed a number of formal systems for expressing the
relationships between linguistic elements in literary texts. One of these systems, FASTL
(Formalismes pour l'Analyse et la Synthèse de Textes Littéraires), used recursion and
iteration to encompass all forms of written communication. It is no accident that, with
Braffort a computer scientist and Roubaud a mathematician, FASTL resembles abstract
systems of representation used in the sciences: “USFAL [‘Un Système Formel pour
l'Algorithmique Linguistique’, a precursor to FASTL] will be for theoretical
literature what mathematical disciplines such as the theory of differential
equations can be for physics, economics, etc.”
[Oulipo 1981, 128]. An important feature of FASTL is its scalability in representing textual
elements. Text objects are organized within a hierarchy that extends from the characters
on a page or screen to entire libraries or corpora. Expressions for representing
relationships between objects at one level of the hierarchy should be applicable to
relationships of objects at another level within the hierarchy. Algorithms for analyzing
texts can potentially operate recursively:
Given the scalability of FASTL and the possibility of recursion, abstract representations of texts within FASTL could potentially undergo further processing and abstraction. The complexity of texts as hierarchically structured objects, however, makes devising an algorithm that operates from the level of the word to that of the sentence, paragraph and chapter extremely difficult. Nevertheless, the ALAMO's research into computer-assisted text analysis envisioned the possibility of computational mise en abîme where the results of analysis can repeatedly feed as textual arguments into algorithmic functions in a theoretically never-ending process. The Oulipo anticipated the potentiality of recursion early in its history: in a report submitted to the Collège de Pataphysique (an institution dedicated to the pursuit of the “the science of exceptions”), the group proclaimed that computers would make possible theFor the mind of a mathematician, we will say that the algorithm is a function that, when applied to [a given text] considered as its argument provides a result. This result is itself a text, but a text with a complex organization that is highly structured with fragments of symbolic texts and readings [...] (Oulipo 1981, 133, author's emphasis.)
The efforts of the ALAMO to develop computer applications that generate texts through recursion have met limited success, however [Braffort 2006].abstracting [of] commonplaces from the structures of commonplaces — and then a “squared” topology of these places, and so forth until one attains, in a rigorous analysis of this regressus itself, the absolute, the Absolute “whose armature,” according to Jarry, “is made of clichés.” [Motte 1986, 50]
As Braffort himself recognized, implicational methods for writing texts with machines
are related to research in artificial intelligence. The Oulipo does not seek to replace
the human writer who is at the center of the Oulipian enterprise. The group's approach
to automating potential literature follows the Cartesian method of dividing a
complicated question (how does writing occur?) into smaller questions that are easier to
solve. Recursion is one technique that could allow humans to pursue new forms of writing
by handing off some of the work to machines. But where is the limit to recursion? In
1963 Jacques Duchateau argued that what interests the the Oulipo most in machines is
their capacity for organizing information:
Duchateau attempts to allay fears of an unintended determinism resulting from the aleatory effects of rigorous textual constraints, but his notion of organization does not make any distinction between humans and machines as information processing units. His definition of organization is recursive: it holds that a machine processes information according to a model based on another machine, which in turn processes information according to the model of another machine, etc. We might be tempted to think that for the Oulipo, humans are the first machines after which all other machines are modeled, but Duchateau's definition places humans and machines on the same ontological footing. Ultimately there is no central processing unit which controls all the subprocesses. The Oulipian inventor may create blueprints for literature, but he distances himself from the work of applying rules and crafting texts. Despite his privileged isolation from the particularities of writing, the inventor is just another process that communicates with other processes.organized means that data will be processed, that all the possibilities of the data will be examined systematically according to a model that will be furnished eventually by man or another machine, the model of which will also be furnished by a third machine, the model of which, etc. (in Bens 1980, 251)
If, as Duchateau explains it, the process of writing literature with machines consists
of organizing information in new ways to analyze and synthesize texts, traditional
authorship will eventually give way to a set of increasingly anonymous and autonomous
processes. During one of its reunions in the early 1960s the Oulipo anticipated the risk
of automatism in the structures they were defining. The group attempted to make room for
individual freedom but they were unable to reconcile freedom with automatism. Jacques
Bens recognized that every structure automatized writing to a certain extent, and Claude
Berge added that potential literature generated new automatisms [Bens 1980, 144]. Le Lionnais insisted that a sufficiently complex system of constraints
offered writers a number of options from which they could choose. The Oulipians wanted
to avoid the unconscious automatisms of the Surrealists, but the conscious use of
structures in their writing produced what they could not avoid describing as “automatic”. Le Lionnais admitted that “it is true that the birth of machines has
modified the current sense of the word ‘automatic’
”
[Bens 1980, 185]. The Oulipo recognized that the problem of using computers to create texts
stemmed from the writer's inability to remain aware of how the machine applied
constraints. In the 1970s the Oulipo introduced the notion of the clinamen, which helped
to resolve this dilemma. Based on a conception of the movement of atoms in Lucretius'
On the Nature of Things, the clinamen is the primordial
anti-constraint: it makes creation possible by introducing chance and spontaneity in an
ordered universe [Motte 1986]. The Oulipo recovered a sense of the
unexpected in the constraints they used but they wanted to define and control how chance
would play in their writing. An algorithm is productive as a tool for engaging texts as
long as the user can follow how the algorithm works and anticipate the effects of
chance. If the computational system becomes too complex or too unpredictable, the act of
interpretation will depend on opaque sequences of data processing of which the user must
remain unconscious.
The Oulipians developed at least two algorithms for reading texts. The first is Harry
Mathews's Algorithm, which consists of combinatoric operations over a set of
structurally similar but thematically heterogeneous texts. These operations generalize
the structure of the Cent mille milliards de poèmes and
allow for the production of new texts. For instance, given four texts each consisting of
nine elements
we can use Mathews's Algorithm to produce four new combinations:
| 1. | a1 | b1 | c1 | d1 | e1 | f1 | g1 | h1 | i1 |
| 2. | a2 | b2 | c2 | d2 | e2 | f2 | g2 | h2 | i2 |
| 3. | a3 | b3 | c3 | d3 | e3 | f3 | g3 | h3 | i3 |
| 4. | a4 | b4 | c4 | d4 | e4 | f4 | g4 | h4 | i4 |
Table 1.
Before applying Mathews's Algorithm| 1. | a1 | b2 | c3 | d4 | e1 | f2 | g3 | h4 | i1 |
| 2. | a4 | b1 | c2 | d3 | e4 | f1 | g2 | h3 | i4 |
| 3. | a3 | b4 | c1 | d2 | e3 | f4 | g1 | h2 | i3 |
| 4. | a2 | b3 | c4 | d1 | e2 | f3 | g4 | h1 | i2 |
Table 2.
After applying Mathews's AlgorithmIn his Exercices de style, Queneau relates the same banal
incident on the Paris bus system ninety-nine times, each instance demonstrating a
particular textual style. By identifying a nine-part structure common to four of the
exercises, one can apply Mathews's Algorithm to generate four new versions of the
incident (see http://bumppo.hartwick.edu/Oulipo/Exercices.html). According to Mathews, the
the aim of the algorithm “is not to liberate potentiality but to
coerce it”
[Motte 1986, 139]. A “new” reading of a text (or a reading of a “new” text) through the
algorithm is not the objective. The use of the algorithm is meaningful in that the
apparent unity of texts can be dismantled and give way to a multiplicity of meanings.
Mathews invented a system of constraints that illustrates what poststructuralists have
maintained for decades.[2]
The second example is Raymond Queneau's matrix analysis of language, published in Etudes de linguistique appliquée and discussed at length during
one of the Oulipo's early gatherings. Using principles of linear algebra, Queneau
devised a mathematics of the French language that describes the construction of word
groups. He began by dividing all elements of speech into two categories: signifiers,
which include nouns, adjectives, and verbs (except avoir and
être); and formatives, which include everything else
(avoir, être, pronouns, articles, conjunctions,
prepositions, adverbs, interjections, etc.). Given a word group such as a sentence, one
can construct two matrices where the first matrix contains all formatives and the second
all signifiers.
If a word group contains two consecutive formatives or signifiers, one can use
a unitary element in order to construct the matrices.
The product of a formative and a signifier is a bi-word. By adopting the
conventions that neither (1 × 1) nor (Y × 1) + (1 × Z) are allowed, one avoids
uninteresting or redundant bi-words. Where Y and Z are any formative and signifier
respectively, we can designate (Y × Z) as B, (Y × 1) as F and (1 × Z) as S. This gives
us BBB for Figure 5 and BSFBBS for Figure 6.
Figure 5.
Queneau's language matrices: A single sentence represented as a product of two
matrices. (The × cat) + (has × eaten) + (the × mouse).
Figure 6.
The unitary element permits the representation of word groups with consecutive
formatives or signifiers. (The × naughty) + (1 × cat) + (has × 1) +
(thoroughly × eaten) + (the × beautiful) + (1 × mouse).
Queneau himself constructed matrices for a number of short sample texts, but his ability
to apply the algorithm to lengthy texts was limited because he did his calculations
manually. With the availability of part-of-speech taggers such as Helmut Schmid's
TreeTagger, it is relatively easy to perform a matrix analysis of any text written in
French with a computer [Schmid 2006]. [3] Consider the
following representation of the first paragraphs of Flaubert's Madame Bovary:
The text is broken down into bracketed pairs representing bi-words,
signifiers, formatives and punctuation. We can transform the text into an abstract
sequence of the letters F, S and B:
Note that punctuation separates word groups. One can compute the
probability ratios of formatives (F), signifiers (S) and bi-words (B) in a text:
F + S + 2B always equals the total number of words in a text.
- [Nous 1][étions 1][à 1][l' Etude][ ,][quand 1][le Proviseur][1 entra]
- [1 suivi][d' 1][un nouveau][1 habillé][en bourgeois][et 1][d' 1]
- [un garçon][de classe][qui portait][un grand][1 pupitre][ .][Ceux 1]
- [qui dormaient][1 se][1 réveillèrent][ ,][et 1][chacun 1][se leva]
- [comme surpris][dans 1][son travail.][Le Proviseur][nous fit][1 signe]
- [de 1][nous rasseoir][ ;][puis 1][ ,][se tournant][vers 1][le maître]
- [d' études][1 :][ -][1 Monsieur][1 Roger][ ,][lui dit][-il 1][à 1]
- [demi-voix 1][ ,][voici 1][un élève][que 1][je 1][vous recommande][ ,]
- [1 il][entre 1][en cinquième][ .][Si 1][son travail][et 1][sa conduite]
- [sont méritoires][ ,][il passera][dans les][1 grands][ ,][où 1][l' appelle]
- [son âge.][1 Resté][dans 1][l' angle][ ,][derrière 1][la porte][ ,][si 1]
- [bien 1][qu' 1][on 1][l' apercevait][à peine][ ,][le nouveau][était 1]
- [un gars][de 1][la campagne][ ,][d' 1][une quinzaine][d' années][environ 1]
- [ ,][et 1][plus 1][haut 1][de taille][qu' 1][aucun 1][de 1][nous tous][ .][Il 1]
- [avait 1][les cheveux][1 coupés][1 droit][sur 1][le front][ ,][comme 1]
- [un chantre][de village][ ,][l' air][1 raisonnable][et 1][fort embarrassé][ .]
- [Quoiqu' 1][il 1][ne 1][fût 1][pas large][1 des][1 épaules][ ,]
- [son habit-veste][de drap][1 vert][à boutons][1 noirs][1 devait][le gêner]
- [aux entournures][et laissait][1 voir][ ,][par 1][la fente][des parements][ ,]
- [des poignets][1 rouges][1 habitués][à 1][être nus.][Ses jambes][ ,][en 1]
- [bas bleus][ ,][1 sortaient][d' 1][un pantalon][1 jaunâtre][très tiré][par 1]
- [les bretelles.][Il 1][était chaussé][de souliers][1 forts][ ,][mal cirés][ ,]
- [1 garnis][de clous][ .]
Example 2.
Madame Bovary as a sequence of word pairsFFFB, FBSSFBSBFFBBBBS. FBSS, FFBBFBBBSFB; F, BFBBS- SS, BFFF, FBFFB, SFB. FBFBB,
BBS, FBBSFB, FB, FFFFBB, BFBFB, FBBF, FFFBFFFB. FFBSSFB, FBB, BSFB. FFFFBSS,
BBSBSSBBBS, FBB, BSSFBB, FB, SFBSBFBFBBS, B, SB.
Example 3.
Abstract reduction of Madame Bovary
| F | = 55 | 0.369127516778524 |
| S | = 28 | 0.187919463087248 |
| B | = 66 | 0.442953020134228 |
Table 3.
Probabilty ratios in the passage from Madame
Bovary
Queneau believed that matrix analysis could provide “indices of an author's style that may be
interesting, for they escape the conscious control of the writer and doubtless
depend on several hidden parameters”
[Queneau 1965, 319]. He did not elaborate further on how one could determine such indices, but his
matrix analysis can be combined with the use of Markov chains in order to measure the
authorship of texts. Given the four letters F, S, B and P to designate formatives,
signifiers, bi-words and punctuation, we can construct a transition matrix of the
probabilities of letter sequences in the passage from Madame
Bovary:
| S | F | B | P | |
| S | 0.23809524 | 0.33333333 | 0.23809524 | 0.19047619 |
| F | 0.00000000 | 0.32727273 | 0.61818182 | 0.05454545 |
| B | 0.17391304 | 0.17391304 | 0.27536232 | 0.37681159 |
| P | 0.12121212 | 0.51515152 | 0.33333333 | 0.03030303 |
Table 4.
Transition matrix of passage from Madame Bovary
Note that the probability of the sequence FS is zero because such a sequence would be an
instance of a bi-word. Dmitri Khmelev and Fiona Tweedie have developed a technique for
determining authorship using Markov chains and transition matrices for the sequence of
letters in a text. Their technique can also be used with formatives, signifiers,
bi-words and punctuation. Given a text of which the author is one of a group of known
authors in a corpus, we can determine the probability that the text in question was
written by each of the known authors. I have used this technique with a corpus of 1569
texts written by 290 authors from the ARTFL database (http://humanities.uchicago.edu/orgs/ARTFL/). I first selected randomly a text
from each author in the corpus. Of the 290 randomly selected texts, 186 were correctly
attributed to the authors who wrote them, or 64 percent. According to Khmelev and
Tweedie, this represents an error rate of 0.153 percent. I then performed a
cross-validation of the ARTFL corpus where 557 texts were correctly classified by
author. These results are similar to those of Khmelev and Tweedie, suggesting that the
combination of matrix analysis and Markov chains offers an interesting technique for
measuring “linguistically
microscopic” data to determine the authorship of texts written in French [Khmelev and Tweedie 2001, 302–4]. [4]
Whatever the promise of matrix analysis in providing quantitative evidence for measuring
an author's style, Queneau expressed greater interest in its mathematical properties. He
proved several theorems on the behavior of matrices and identitified similarities
between them and the Fibonacci series [Queneau 1964]. He also explored the
potentiality of matrices without basing his analyses on written texts. He and the other
members of the Oulipo were intrigued by matrix analysis but looked forward to the
creation of poems written in columns and rows (in Bergens
61–66):
Matrix analyis can help discover the “hidden parameters” of an author's
style, and we could consider it as an interesting example of Anoulipism, or the
discovery of potentialities in existing texts. The Oulipo believed, however, that
Synthoulipism, or the invention of potentialities for future texts, was its “essential vocation”
[Motte 1986, 27]. The combinatorics of non-linear, two-dimensional poems invite the development of
computer applications for generating and analyzing a new kind of text. Whether anyone
will go to the trouble to write and read matrix poems is a question the Oulipo has not
pursued, in part perhaps because it would involve realizing concrete techiques and
practices that would no longer be potential.
Figure 7.
By factoring rows from the first matrix (formatives) with columns from the
second matrix (signifiers), the reader can generate 4 × 3 = 12 distinct verses:
Les enfants ont à manger de la tarte et des gaufrettes. The children have to eat pie and cookies.Des chèvres avaient à brouter de la sauge et des muguets. Some goats were supposed to graze on sage and liliesCes Chinois auront à manipuler de la langoustine et des baguettes. These Chinese will have to handle shrimp and chopsticks.Certains enfants eurent à manger de la tarte et des gaufrettes. Certain children had to eat pie and cookies.Les chèvres ont à brouter de la sauge et des muguets. The goats have to graze on sage and lilies.Des Chinois avaient à manipuler de la langoustine et des baguettes. Some Chinese were supposed to handle shrimp and chopsticks.Ces enfants auront à manger de la tarte et des gaufrettes. These children will have to eat pie and cookies.Certains chèvres eurent à brouter de la sauge et des muguets. Certain goats had to graze on sage and lilies.Les Chinois ont à manipuler de la langoustine et des baguettes. The Chinese have to handle shrimp and chopsticks.Des enfants avaient à manger de la tarte et des gaufrettes. Some children were supposed to eat pie and cookies.Ces chèvres auront à brouter de la sauge et des muguets. These goats will have to graze on sage and lilies.Certains Chinois eurent à manipuler de la langoustine et des baguettes. Certain Chinese had to handle shrimp and chopsticks.
Mathews and Queneau offer two algorithms for creating meaning with language that
demonstrate the Oulipo's efforts to imagine potentialities for literature, “if need be through recourse to machines that
process information”
[Motte 1986, 27]. We can operationalize these algorithms with computers for literary analysis
“if need be”, but the interest of the
algorithms lies not in what they help us see in a given text but in the way they invite
us to play rigorously for play's sake. Recent efforts to reconceptualize text analysis
with computers have tried to imagine how computers can be used as tools for discovering
new ways to make sense of texts. The Oulipo proposes something more radical: to borrow a
turn of phrase from Jerome McGann, the invention of algorithms can create potentialities
for imagining what we do not know about textuality in general. Given a rigorous
constraint on the use of language, what does the constraint itself do? Does it offer new
possibilities for meaning? If so, how? Oulipian constraints are better understood as
toys with no intended purpose rather than as tools we use with some objective in mind.
The procedures for making sense of texts are meaningful in and of themselves. They are
not only instruments for discovering meaning but also reflections on making meaning. The
distinction I have made between writing and reading follows what the Oulipo has and has
not articulated in the theory of its practice. In the end, the need for a distinction
becomes unnecessary when one observes that all encounters with textuality invite the
application of rules that lead the writer and the reader to see unanticipated
potentialities in language. Humanities computing should make room for playing with tools
without concern for specific output and outcomes. In doing so, it will open itself to
new theoretical possibilities of textuality, creating opportunities for what we could
call “pure” research that may (or may not) draw interest from the broader
humanities community.
Notes
[1] Jerome McGann
observes that “In this view of the
matter, texts and documents are not primarily understood as containers or even
vehicles of meaning. Rather, they are sets of instantiated rules and algorithms
for generating and controlling themselves and for constructing further sets of
transmissional possibilities”
[McGann 2001a, 2].
[2] Readers can use Mathews's Algorithm with their own texts;
see http://bumppo.hartwick.edu/Oulipo/Mathews.php.
[3] Readers can perform Queneau's
matrix analysis using their own texts; see http://bumppo.hartwick.edu/Oulipo/Matrix.html.
[4] Khmelev and Tweedie selected randomly one
text from each of the forty-five authors in their corpus of 387 texts from the
Project Gutenberg (http://www.gutenberg.org/). Thirty-three texts were attributed to the
correct author, or 73.3 percent, with an error rate of 0.687 percent. Like the sample
used by Khmelev and Tweedie, my corpus is composed of texts by authors with at least
two texts in the corpus. This allowed me to test Queneau matrices and Markov chains
as the basis of an algorithm for identifying authorship.
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Last updated:
Comments: dhqinfo@digitalhumanities.org
Published by: The Alliance of Digital Humanities Organizations
Affiliated with: Literary and Linguistic Computing
Copyright 2005 -
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.