We Can Re-Use It For You Wholesale
Serendipity and Objets Trouvés in Linguistic Creativity
Tony Veale
School of Computer Science and Informatics
University College Dublin, Belfield D4, Ireland.
Tony.Veale@UCD.ie
Abstract
The objet trouvé or found object has become a staple of
modern art, one which demonstrates that artistic creativity
is just as likely to arise from serendipitous encounters
in the real world as it is from purposeful exploration
in the studio. These readymades, like
Duchamp’s Fountain (a urinal!) seem banal in their
conventional contexts of use, but take on a new resonance
and meaning when viewed in an artistic space.
This paper considers the linguistic equivalent of the
readymade object: a well-formed phrase that takes on a
new significance when used in a new context with a
potentially new and resonant figurative meaning. We
show how linguistic readymades can be recognized and
harvested on a large scale, and used to provide a robust
and scalable form of creative language generation.
For Best Results, Just Add Water
Computationalists in the classical AI tradition generally
prefer to model the creative process as a purposeful exploration
of a conceptual space (e.g., see Boden, 1994). This
concentration of computational effort makes good engineering
sense, but little artistic sense, since much of what
we consider to be creative insight occurs not in the studio
or the laboratory but through everyday interaction with the
real world. Serendipitous discovery is thus unlikely to arise
in purposeful explorations, since specific applications have
no remit beyond the immediate concerns of their programming
(and programmers), and have no other lives to
live when not actively pursuing these concerns.
The objet trouvé or found object is perhaps the most
potent example of serendipity in artistic creation. An artist
encounters an object with aesthetic merits that are overlooked
in its banal, everyday contexts of use, yet when this
object is moved to an explicitly artistic context, such as an
art gallery, viewers are better able to appreciate these merits.
The transformational power of a simple context switch
is most famously demonstrated by the case of Marcel
Duchamp’s Fountain, a humble urinal that becomes an
elegantly curved piece of sculpture when viewed with the
right mindset. Duchamp referred to his objets trouvés as
“readymades”, since they allow us to remake the act of
artistic creation as one of pure insight and inspired recognition
rather than one of manual craftsmanship (see Taylor,
2009). In computational terms, the Duchampian notion of a
readymade allows artistic creativity to be modeled not as a
construction problem but as a decision problem. A computational
Duchamp need not explore an abstract conceptual
space of potential ideas. Rather, the issue instead becomes:
how do we expose our Duchampian agent to the
multitude of potentially inspiring real-world stimuli that a
human artist encounters everyday?
Readymades represent a form of creativity that is poorly
served by exploratory models of creativity, such as that of
Boden (1994), and better served by the investment models
such as the buy-low-sell-high theory of Sternberg and Lubart
(1995). In this view, creators and artists find unexpected
or untapped value in unfashionable objects or ideas
that already exist, and quickly move their gaze elsewhere
once the public at large come to recognize this value.
Duchampian creators invest in everyday objects, just as
Duchamp found artistic merit in bottles and combs. From a
linguistic perspective, these everyday objects are commonplace
words and phrases which, when wrenched from their
conventional contexts of use, are free to take on enhanced
meanings and provide additional returns to the investor.
The realm in which a maker of linguistic readymades operates
is not the real world, and not an abstract conceptual
space, but the realm of texts: large corpora become rich
hunting grounds for investors in linguistic objets trouvés.
This proposal is realized in a computational form in the
following sections. A rich vocabulary of cultural stereotypes
is acquired from the web, and it is this vocabulary
that facilitates the implementation of a decision procedure
for recognizing potential readymades in large corpora – in
this case, the Google database of web ngrams (Brants and
Franz, 2006). This decision procedure provides the basis
for a robust web application called The Jigsaw Bard, and
the cognitive insights that underpin The Bard’s conception
of linguistic readymades are then put to the empirical test
using statistical analysis. While readymades remain a contentious
notion in the public’s appreciation of artistic creativity
– despite Duchamp’s Fountain being considered one
of the most influential artworks of the 20th century – we
Proceedings of the Second International Conference on Computational Creativity 22
shall show that the notion of linguistic readymade has significant
practical merit in the realms of text generation and
computational creativity.
A Modest Proposal
Readymades are the result of artistic appropriation, in
which an object with cultural resonance – an image, a
phrase, a thing – is re-used in a new context with a new
meaning. As a fertile source of cultural reference points,
language is an equally fertile medium for appropriation.
Thus, in the constant swirl of language and culture, movie
quotes suggest song lyrics, which in turn suggest movie
titles, which suggest book titles, or restaurant names, or the
names of racehorses, and so on, and on. The 1996 movie
The Usual Suspects takes its name from a memorable
scene in 1942’s Casablanca, as does the Woody Allen play
and movie Play it Again Sam. The 2010 art documentary
Exit Through the Gift Shop, by graffiti artist Banksy, takes
its name from a banal sign sometimes seen in museums
and galleries: the sign, suggestive as it is of creeping commercialism,
makes the perfect readymade for a film that
laments the mediocrity of commercialized art.
Appropriations can also be combined to produce novel
mashups; consider, for instance, the use of tweets from
rapper Kanye West as alternate captions for cartoons from
the New Yorker magazine (see the hashtag #KanyeNewYorkerTweets).
Hashtags can themselves be linguistic
readymades. When free-speech advocates use the hashtag
#IAMSpartacus to show solidarity with users whose tweets
have incurred the wrath of the law, they are appropriating
an emotional line from the 1960 film Spartacus. Linguistic
readymades, then, are well-formed and highly quotable
text fragments, that carry some figurative content which
can be reused and revitalized in many different contexts. In
this spirit, the title of this paper, and all of its section
headings, are readymades of varying provenance.
Naturally, a quote like “round up the usual suspects” or
“I am Spartacus” requires a great deal of cultural knowledge
to appreciate. Since literal semantics only provides a
small part of their meaning, a computer’s ability to recognize
linguistic readymades is only as good as the cultural
knowledge at its disposal. We need to explore a more
modest form of readymade, as in the following phrases:
a wet haddock
snow in January
a robot fish
a bullet-ridden corpse
Each phrase can be found in the Google database of web
ngrams, and each is likely a literal description of a real
object or event – even “robot fish”, which describes an
autonomous marine vehicle whose movements mimic real
fish. But each exhibits figurative potential as well, providing
a memorable description of physical or emotional
coldness. Whether or not each was ever used in a figurative
sense before is not the point: once this potential is recognized,
each phrase becomes a reusable linguistic readymade
for the construction of a vivid figurative comparison,
as in “as cold as a robot fish”. We now consider the
building blocks from which these comparisons can be
ready-made.
Round Up The Usual Suspects
How does a computer acquire the knowledge that fish,
snow, January, bullets and corpses are cultural signifiers of
coldness, and that “heartless” robots in particular are cultural
signifiers of emotional iciness? Much the same way
that humans acquire this knowledge: by attending to the
way these signifiers are used by others, especially when
they are used in cultural clichés like proverbial similes
(e.g., “as cold as a fish”).
In fact, folk similes are an important vector in the transmission
of cultural knowledge: they point to, and exploit,
the shared cultural touchstones that speakers and listeners
alike can use to construct and intuit meanings. Taylor
(1954) catalogued thousands of proverbial comparisons
and similes from California, identifying just as many
building blocks in the construction of new phrases and
figurative meanings. Only the most common similes can be
found in dictionaries, as shown by Norrick (1986), while
Moon (2008) demonstrates that large-scale corpus analysis
is needed to identify folk similes with a breadth approaching
that of Taylor’s study. However, Veale and Hao (2007)
show that the world-wide-web is the ultimate resource for
harvesting similes.
Veale and Hao use the Google API to find many instances
of the pattern “as ADJ as a|an *” on the web,
where ADJ is an adjectival property and * is the Google
wildcard. WordNet (Fellbaum, 1998) is used to provide a
set of over 2,000 different values for ADJ, and the text
snippets returned by Google are parsed to extract the basic
simile bindings. Once the bindings are annotated to remove
noise, as well as frequent uses of irony, this web harvest
produces over 12,000 cultural bindings between a noun
(such as fish, or robot) and its most stereotypical properties
(such as cold, wet, stiff, logical, heartless, etc.). Stereotypical
properties are acquired for approx. 4,000 common
English nouns. This is a set of building blocks on a larger
scale than even that of Taylor, allowing us to build on
Veale and Hao (2007) to identify linguistic readymades in
their hundreds of thousands in the Google ngrams.
However, to identify readymades as resonant variations
on cultural stereotypes, we need a certain fluidity in our
treatment of adjectival properties. The phrase “wet haddock”
is a readymade for coldness because “wet” accentuates
the “cold” that we associate with “haddock” (via the
web simile “as cold as a haddock”). In the words of Hofstadter
(1995), we need to build a SlipNet of properties
whose structure captures the propensity of properties to
mutually and coherently reinforce each other, so that
phrases which subtly accentuate an unstated property can
be recognized. In the vein of Veale and Hao (2007), we use
the Google API to harvest the elements of this SlipNet.
Specifically, we hypothesize that the construction “as
ADJ1 and ADJ2 as” shows ADJ1 and ADJ2 to be mutually
Proceedings of the Second International Conference on Computational Creativity 23
reinforcing properties, since they can be seen to work together
as a single complex property in the context of a single
comparison. Thus, using the full complement of adjectival
properties used by Veale and Hao (2007), we harvest
all instances of the patterns “as ADJ and * as” and “as *
and ADJ as” from Google, noting both the combinations
that are found and their relative frequencies. These frequencies
provide the link weights for the Hofstadter-style
SlipNet that is then constructed. In all, over 180,000 links
are harvested, connecting over 2,500 adjectival properties
to each other. We put the intuitions behind this SlipNet to
the empirical test in a later section.
We Can Remember It For You, Wholesale
In the course of an average day, a creative writer is exposed
to a constant barrage of linguistic stimuli, any small
portion of which can strike a chord as a potential readymade.
In this casual inspiration phase, the observant writer
recognizes that a certain combination of words may produce,
in another context, a meaning that is more than the
sum of its parts. Later, when an apposite phrase is needed
to strike a particular note, this combination may be retrieved
from memory (or from a trusty notebook), if it has
been recorded and suitably indexed.
Given a rich vocabulary of cultural stereotypes and their
properties, computers are capable of indexing and recalling
a considerably larger body of resonant combinations than
the average human. The necessary barrage of stimuli can
be provided by the Google 1T database of web ngrams –
snippets of web text (of one to five words) that occur on
the web with a frequency of 40 or higher (Brants and
Franz, 2006). Trawling these ngrams, a modestly creative
computer can recognize well-formed combinations of cultural
elements that might serve as a vivid vehicle of description
in a future comparison. For every phrase P in the
ngrams, where P combines stereotype nouns and/or adjectival
modifiers, the computer simply poses the following
question: is there an unstated property A such that the simile
“as A as P” is a meaningful and memorable comparison?
The property A can be simple, as in “as dark as a
chocolate espresso”, or complex, as in “as dark and sophisticated
as a chocolate martini”. In either case, the
phrase P is tucked away, and indexed under the property A
until such time as the computer needs to produce a vivid
evocation of A.
The following patterns are used to identify potential
readymades in the web ngrams:
(1) NounS1 NounS2
where both nouns denote are stereotypes that share an unstated
property AdjA. The property AdjA serves to index
this combination. Example: “as cold as a robot fish”.
(2) NounS1 NounS2
where both nouns denote stereotypes with salient properties
AdjA1 and AdjA2 respectively, such that AdjA1 and
AdjA2 are mutually reinforcing. The combination is indexed
on AdjA1+AdjA2. Example: “as dark and sophisticated
as a chocolate martini”.
(3) AdjA NounS
where the noun is a known stereotype, and the adjective is
a property that mutually reinforces an unstated, but salient,
property of the stereotype. Example: “as cold as a wet haddock”.
The combination is indexed on this property.
Other, syntactically richer structures for P are also possible,
as in the phrases “a lake of tears” (a melancholy way
to accentuate the property “wet”) and “a statue in a library”
(for “silent” and “quiet”). In this current work, we
shall focus on 2-gram phrases only.
Using these patterns, our application – the Jigsaw Bard
– pre-builds a vast collection of figurative similes well in
advance of the time it is asked to use or suggest any of
them. Each phrase P is syntactically well-formed, and because
P occurs relatively frequently on the web, it is likely
to be semantically well-formed as well. Just as Duchamp
side-stepped the need to physically originate anything, but
instead appropriated pre-built artifacts, the Bard likewise
side-steps the need for natural-language generation. Each
phrase it proposes has the ring of linguistic authenticity;
because this authenticity is rooted in another, more literal
context, the Bard also exhibits its own Duchamp-like (if
Duchamp-lite) creativity. We now consider the scale of the
Bard’s generativity, and the quality of its insights.
Use Only The Finest Ingredients
The vastness of the web, captured in the large-scale sample
that is the Google ngrams, means the Jigsaw Bard finds
considerable grist for its mill in the phrases that match
(1)…(3). Thus, the most restrictive pattern, pattern (1),
harvests approx. 20,000 phrases from the Google 2-grams,
for almost a thousand simple properties (indexing an average
of 29 phrases under each property, such as “swan
song” for “beautiful”). Pattern (2) – which allows a blend
of stereotypes to be indexed under a complex property –
harvests approx. 170,000 phrases from the 2-grams, for
approx. 70,000 complex properties (indexing an average of
12 phrases under each, such as “hospital bed” for “comfortable
and safe”). Pattern (3) – which pairs a stereotype
noun with an adjective that draws out a salient property of
the stereotype – is similarly productive: it harvests approx.
150,000 readymade phrases for over 2,000 simple properties
(indexing an average of 125 phrases per property, as in
“youthful knight” for “heroic” and “zealous convert” for
“devout”).
The Jigsaw Bard is best understood as a creative thesaurus:
for any given property (or blend of properties) selected
by the user, the Bard presents a range of apt similes constructed
from linguistic readymades. The numbers above
show that, recall-wise, the Bard has sufficient coverage to
Proceedings of the Second International Conference on Computational Creativity 24
work robustly as a thesaurus. Quality-wise, users must
make their own determinations as to which similes are
most suited to their descriptive purposes, yet it is important
that suggestions provided by the Bard are sensible and
well-motivated. As such, we must be empirically satisfied
about two key intuitions: first, that salient properties are
indeed acquired from the web for our vocabulary of
stereotypes (this point relates directly to the aptness of the
similes suggested by the Bard); and second, that the adjectives
connected by the SlipNet really do mutually reinforce
each other (this point relates directly to the coherence of
complex properties, as well as to the ability of readymades
to accentuate an unstated property).
Both intuitions can be tested using Whissell’s (1989)
dictionary of affect, a psycholinguistic resource used for
sentiment analysis that assigns a pleasantness score of between
1.0 (least pleasant) and 3.0 (most pleasant) to over
8,000 commonplace words. We should thus be able to predict
the pleasantness of a stereotype noun (like fish) using a
weighted average of the pleasantness of its salient properties
(like cold, slippery). We should also be able to predict
the pleasantness of an adjective using a weighted average
of the pleasantness of its adjacent adjectives in the SlipNet.
(In each case, weights are provided by relevant web frequencies.)
We can use a two-tailed Pearson test (p < 0.05)
to compare the predictions made in each case to the actual
pleasantness scores provided by Whissell’s dictionary, and
thereby assess the quality of the knowledge used to make
the predictions. In the first case, predictions of the pleasantness
of stereotype nouns based on the pleasantness of
their salient properties (i.e., predicting the pleasantness of
Y from the Xs in “as X as Y”) have a positive correlation
of 0.5 with Whissell; conversely, ironic properties yield a
negative correlation of –0.2. In the second, predictions of
the pleasantness of adjectives based on their relations in
the SlipNet (i.e., predicting the pleasantness of X from the
Ys in “as X and Y as”) have a positive correlation of 0.7.
Though pleasantness is just one dimension of lexical affect,
it is one that requires a broad knowledge of a word
and its usage to accurately estimate. In this respect, the
Bard is well served by a large stock of stereotypes and a
coherent network of informative properties.
So Long, And Thanks For All The Robotic Fish
Fishlov (1992) has argued that poetic similes represent a
conscious deviation from the norms of non-poetic comparison.
His analysis shows that poetic similes are longer
and more elaborate, and are more likely to be figurative
and to flirt with incongruity. Creative similes do not necessarily
use words that are longer, or rarer, or fancier, but use
many of the same cultural building blocks as non-creative
similes. Armed with a rich vocabulary of building blocks,
the Jigsaw Bard harvests a great many readymade phrases
from the Google ngrams – from the evocative “chocolate
martini” to the seemingly incongruous “robot fish” – that
can be used to evoke an equally wide range of properties.
This generativity makes the Bard scalable and robust.
However, any creativity we may attribute to it comes not
from the phrases themselves – they are readymades, after
all – but from the recognition of the subtle and often complex
properties they evoke. The Bard exploits a sweet-spot
in our understanding of linguistic creativity just as much as
Duchamp and his followers exploited a sweet-spot in the
public’s understanding of art and how it is practiced. But
as presented here, the Bard is a starting point on our exploitation
of linguistic readymades, and not an end in itself.
By harvesting more complex syntactic structures, and using
more sophisticated techniques for analyzing the figurative
potential of these phrases, the Bard and its ilk may
gradually approach the levels of poeticity discussed by
Fishlov. For now, it is sufficient that even simple techniques
serve as the basis of a robust and practical thesaurus
application.
Exit Through The Gift Shop
A screenshot of The Jigsaw Bard is presented in Figure 1
(see overleaf). The application can be accessed online at:
http://www.educatedinsolence.com/jigsaw
<references_biblio/>
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Proceedings of the Second International Conference on Computational Creativity 25
Figure 1. Screenshot of The Jigsaw Bard, retrieving linguistic readymades on demand.
Proceedings of the Second International Conference on Computational Creativity 26