Abstract

The story of a tweet
At 6:00 PM, 3 December, 2016, @OmarjSakr, a bisexual, second-generation American from Lebanese-Turkish Muslim migrant parents, a poetry editor, an author, and a self-styled “geeky progressive,” tweeted the following:
Women in Middle East attacked for not wearing hijab. Women in the West attacked for wearing hijab. It’s almost like women aren’t the problem. [48]
Then he went about his regular, non-Twitter affairs. The next afternoon, fresh from a nap, he checked his account, stared at the outcome, and tweeted to @upulieto, an associate, “I just woke up. What a mess” [49]. His tweet had spiraled virally: tens of thousands of likes, retweets, endless streams of commentary. Why?
Well, lots of factors, of course. There is rarely a single factor to which one can point in rapid cultural propagation. But the pithiness of the tweet, its form/function iconicity, and its argumentative force are surely three of those factors – perhaps the three most important – and all of them are a direct consequence of the tweet’s rhetorical figures. Its figures notably capitalize on repetition, a stylistic move one might think is anathema to twitter. Repetitions are highly redundant, contributing very little new information. They should therefore be corrosive to the concision so necessary to effective tweets. Repetitions distend the ideational function, not condense it, making them very costly in twitter, which provides only a 140-character vehicle. And yet: the tweet was instantly and broadly recognized as a brilliant, incisive, necessarily brief and highly effective argument.
Most notable among its figuration is the compound figure, symploce, in which the same word or words are repeated at the beginnings and endings of proximal clauses or phrases (Women in and wearing hijab). Symploce is a compound figure because it is a combination of epanaphora (phrase- or clause-initial lexical repetition; example 2) and epistrophe (phrase- or clause-final lexical repetition; example 3), the two of them often sandwiching similar or identical phrasing (as in example 1 and also 5)
On October 29, we again entered the Palace of Justice; again the crowds were large and excited; again the security was extremely tight; again the court was filled with dignitaries from many foreign embassies. ([43]: p. 354) Now, no matter what the mullah teaches, there is only one sin, only one. And that is theft. Every other sin is a variation of theft. ([28]: p. 17)
These figures of lexical repetition, distinguished by location, get their salience, their memorability, and their aesthetic effects because of human neurocognitive affinities. We naturally respond to repetitions because they re-activate recently active neural pathways. We naturally attend to category boundaries because our minds are tuned to categorization and because our perceptions are tuned to ‘edges.’ But figures don’t ‘merely stimulate’ our mindbrains. They aren’t ‘mere decorations’ that put a little rouge on the cheeks of otherwise ‘pure information.’ They serve communicative functions (including pragmatic, suasive, and argumentative functions), in addition to their aesthetic and mnemonic functions.
Figures of lexical repetition are types of ploce. Simple ploce, usually just called ploce, is unconstrained lexical repetition. Its primary function is to ensure stability of reference. One repeats the same word to evoke the same concept. Complex ploce is constrained by position, and includes symploce, epanaphora, and epistrophe, which all serve theme-and-variation communicative functions. Complex ploce include both stable elements (because they are varieties of ploce), to ensure a consistency of reference, and variable elements, to associate that referential stability with a circumscribed semantic range. Epanaphora is a figure (in English) that features a stable framing of variable predications; in the Mandela example (2), each of the predications is framed in terms of an earlier event (that is, an earlier trial).1
Notice that for position-sensitive figures of lexical repetition language typology is important. English is a Subject-Verb-Object word-order language, so clause-initial epanaphora will tend strongly towards stabilizing subject reference, while clause-final epistrophe will tend strongly towards stabilizing object reference. Other figures, such as simile (a cross-domain comparison) and polyptoton (repetition of lexical stems with different morphology), are more uniform in their cross-linguistic functions.
All humans are mortal.
Mr. Jones is a human.
Therefore, Mr. Jones is mortal
Symploce includes two bands of stability, putting the medial lexis into a kind of referential vise. The following prototypical instance of symploce (5), from Malcolm X, argues for the inexorability of racism played out in American martial geopolitics: the reference of the medial lexis is irrelevant – Germany, the South Pacific, Japan, wherever – the white man uses the black man as cannon fodder.
[As] long as the white man sent you to Korea, you bled. He sent you to Germany, you bled. He sent you to the South Pacific to fight the Japanese, you bled. [42] We used to joke that between the poor acoustics of the hall and the confused and inaccurate reports of the Special Brand detectives, we could be fined for what we did not say, imprisoned for what we could not hear, and hanged for what we did not do. ([43]: p. 232) The young would choose an exciting life; the old a happy death. ([1]: p. 155) Have faith in your fellow citizens my friends, they are kind and generous. They are open minded and optimistic and they know in their heart of hearts that a Canadian is a Canadian is a Canadian. [61]
@OmarjSakr’s tweet has this vise-like quality as well, but there are three other figures highly relevant to its argumentative functioning: mesodiplosis (medial lexical repetition; in the tweet, attacked for; in 6 below, what we did not, with the slight variation of what we could not; it also occurs in 8 with is a); antithesis (proximal opposing predications; in the tweet, not wearing hijab and wearing hijab; in 7, young… life and old… death); and (simple) ploce (the brute lexical repetition in the @OmarjSakr tweet of women; in 8, of Canadian).
Mesodiplosis unites the terms on either side of the repetition (fine and not say, imprison and not hear, hang and not do in 6, as well as the ploce a Canadian of 8), here emphasizing the capricious malevolence of the apartheid system (6) and the utter identity of Canadian citizenship (8). Antitheses present binaries and/or polarities to push terms apart, especially a double antithesis like 7, which drives the polarities young and old further apart by associating them respectively with the binary opposites, life and death. And the main function of ploce is to guarantee stability of reference (often full identity of reference) – in 8, the relentless insistence that there are no differences that matter with regards to citizenship if you are Canadian.
In order to see all of these figural functions at work in @OmarjSakr’s tweet, we can render its component sentences as so:
Women in Middle East [are] attacked for not wearing hijab. Women in the West [are] attacked for wearing hijab. It’s almost like women aren’t the problem.
Sentences 9a and 9b are the symploce and they also manifest the binary antithesis of wearing/not wearing the hijab, and all three sentences (9a, 9b, and 9c) manifest the ploce of women. So, we end up with an ad absurdum (distilling out Middle East and West and assuming problem throughout):
Rendered this way, we see a more precise distribution of figures. We have the epanaphora of
We haven’t fully explicated the argumentative activity of the tweet, of course, because there is also another figurative dimension (namely, the note of irony with almost like), and because the argument is enthymematic, with the covert premise that men are responsible for the attacks (so that the presence or absence of the hijab is just an excuse to exercise power), and because we have only charted the overt style. But we do have its logical force. One of the primary functions of antithesis, when the opposing predicates are binary, rather than polar, and when it couples referential-stabilizing ploces, is to signal a paradox, and that’s what it does in this tweet. ‘The situation is absurd,’ @OmarjSakr is saying. ‘Women are attacked for x and attacked for
There are at least two morals here, in the story of @OmarjSakr’s tweet, about the role of rhetorical figures in argumentation. Firstly, figures activate neurocognitive affinities. Figures are salient, aesthetically appealing, and memorable because they excite fundamental neurocognitive principles, like repetition and boundary prominence. Secondly, structural iconicity signals function. Repetition not only gives textual elements presence, in Perelman’s sense ([52]: p. 17–18), it also guarantees a stability of reference. Endings and beginnings not only give textual elements presence, they also play crucial syntactic roles (which, of course, vary with the syntactic conventions of languages, but vary with considerable predictability). Rhetorical figures, in short, play central roles in argumentation, as the papers in this special issue amply demonstrate.
But Argument is only one of the defining terms of our journal’s title. The other is Computation.
Rhetorical figures, patterns that are present in all languages, reflecting both neurocognitive affinities and communicative imperatives, are linguistic devices that serve mnemonic and aesthetic purposes, alongside informational and argumentational purposes. As examples 1–10 illustrate clearly, they very frequently do so in clusters, sometimes in quite dense clusters, of the sort we find in syllogisms. All three of these characteristics: the affinities, the imperatives, and the clustering make them immensely valuable for computational research into language and argumentation.
Our opening example for instance – @OmarjSakr’s 3 December, 2016, tweet about women, oppression, and patriarchal misogyny (1, 9, 10) – incorporates the neurocognitive affinity of repetition and the communicative imperative of stabilizing reference. Repetition is such a fundamental aspect of neurocognition that we literally could not think without it. For that matter, we could not even breathe, our hearts couldn’t beat, our cells would deteriorate, without the repeated firing patterns of our neurons. It’s no wonder we resort to repetition in trying to remember a name, a phone number, an address, repeating them to ourselves vocally or subvocally. Repetition is a sine qua non of our brains. Stability of reference is so fundamental a communicative imperative that we literally could not communicate without it. Even animal communication systems with such broad referents as, in the vervet monkey call, ‘danger from above’ or, in the bumblebee dance, ‘food source lots of wing-beats hence from the hive,’ would fail utterly without a system that guaranteed the same noises or dance moves evoked the same concepts. @OmarjSakr’s tweet guarantees stability of reference through repetition: women evokes the same concept in all three sentences, attacked and wearing hijab evoke the same concepts in two of them, and so on. The links between affinities and imperatives are not always as tight as between repetition and stability, but they can always be traced, which is what makes rhetorical figures so valuable computationally. We want to be very clear about this: because the patterns are motivated by neurocognitive affinities, they are more robustly linked to functions and meanings than the patterns of familiar syntactic analyses, even Construction Grammar, which all operate on assumptions of arbitrariness; and because they can be charted computationally, in addition to their practical value, they help us to move back towards the originary goal of Artificial Intelligence, using computers to tell us fundamental truths about the mind.
The tweet also activates the neurocognitive affinities of contrast (East, West; not wearing, wearing) and demarcation (women and hijab both appear at clausal boundaries), and the communicative imperatives, among others, of negation (not, aren’t) and predication (in Middle East and in the West predicated of women, etc.).
Deploying affinities and realizing imperatives, rhetorical figures form into recurrent patterns: epanaphora, repetitions at the beginnings of clauses or phrases; epistrophe, repetitions at the ends of clauses or phrases; mesodiplosis, medial repetitions between distinct elements; antithesis, proximal opposing predications. The patterns and the functions correlate, which can provide computer algorithms with data on all aspects of discursive action, prominently including argumentation.
Rhetorical figures solidify and propagate arguments in revealing ways, as a major paper by Jeanne Fahnestock demonstrates. The paper, entitled “Preserving the Figure,” focuses on scientific argumentation and charts the ways in which certain figures are not just used by primary research scientists reporting on their work, but are also picked up and circulated through other reports of that research [19].2
Other work by Fahnestock that repays the attention of argumentation theorists include her “Figures of Argument” [20] and Rhetorical Figures in Science [17]. Her project of Figural Logic is an important advance in argumentation studies (see [60]: p. 69–85, [26]).
We were elected to change Washington, and we let Washington change us. (John McCain [38])
In politics, there are some candidates who use change to promote their careers. And then there are those, like John McCain, who use their careers to promote change. (Sarah Palin [38])
We need a president who puts the Barney Smiths before the Smith Barneys. (Barney Smith [38])
He has brought change to Washington, but Washington hasn’t changed him. (Barack Obama, of Joe Biden [38])
In the end the true test is not the speeches a president delivers, it’s whether the president delivers on the speeches. (Hillary Clinton [38])
Ask not what your country can do for you. Ask what you can do for your country. [33]
See [56] for some discussion.
The 2016 US election, on the other hand, was not so much about wholesale change: Hillary Clinton largely represented a continuation of President Barack Obama’s trajectory while Donald Trump represented a radical dismantling of the policies and programmes defining that trajectory. One suspects that antithesis was highly prevalent in the campaign, as the two candidates represented themselves as binary opposites (fit, unfit; honest, dishonest), and thinking only of Trump’s rhetoric, hyperbole (best, single greatest, largest, worst, … ever, in the country, in the world, in history) and epithets (Little Marco, Lyin’ Ted, Crooked Hillary) would seem highly prevalent as well. But, here’s the thing: the research hasn’t been done. Even the 2008 campaign, the year ‘of the reversible raincoat’ [38] as chiastic figures are sometimes nicknamed, was diagnosed with conventional humanist methods. Computationally, with the right algorithms we could characterize campaigns, speakers, genres, argument styles, virtually any discursive product, process, mode or episode, on the basis of its rhetorical figures; with the right knowledge base, of how figures work together, how they combine with grammatical constructions and lexical properties, and what their functional propensities are, we could understand those discourses at a deeper level than we have so far been able to manage computationally.
Fahnestock’s work in “Preserving the Figure” [19] can be seen as a pilot study. As she points out, her results confirm the expectations of rhetorical theory: “[figures] are typically used in the process of conveying the core of an argument in research articles and in versions of these articles constructed for wider audiences” ([19]: p. 14). But she looked at only pairs of articles, not multiple iterations, and only at 26 pairs in total, and only in scientific argumentation, and only in two genres. The broader scope available to computational research tools could go much further in testing the role of figures in the propagation of arguments.
We could find out, for instance, if Fahnestock’s observation generalizes more broadly, if arguments frequently travel by way of figural realizations. As one test case for her thesis, we might look to see the discursive life of Gregor Mendel’s formula for the distribution of dominant and recessive traits in his pea experiments (example 17), which centrally features a chiastic inversion of the case of a variable (the majuscule and miniscule versions of A switch places in the middle two ratios).
Certainly some of the reports on Mendel’s experiment in the early propagation of his work after its ‘rediscovery’ in the early twentieth century (e.g. [3]: p. 73–74) reproduce this formula, but how many and in which contexts? Is it still preserved, in some version, in contemporary textbooks? Corpus searches could answer questions like these for figures and their functions much more fully than traditional humanist scholarship could reveal, and tell us a good deal about argumentation in the bargain. Note that 17 is not an equation, not an expression of some relationship among theoretical entities (like, say,
But research of this sort has not been done, and the computational research of rhetorical figures in argumentation is just now in its very earliest stages.
Despite its ancient foundations ([51]: p. 26–31) and some remarkable work in the twentieth century, with Fahnestock’s programme of Figural Logic leading the pack ([17,19,20,60], [51]: p. 169–181, [55,60]), rhetoricians and argumentation scholars have been very slow to catch on to the role of rhetorical figures in argumentation. How slow might be diagnosed by the two volumes of the Proceedings of the 1st European Conference on Argumentation [47]. The volumes include some 150 papers and commentaries, from the work of PhD students to the foremost scholars in argumentation studies, and aspire to provide “the most complete statement of the state of the art of argumentation studies today… [f]rom ancient rhetoric to Artificial Intelligence” (according its back-cover blurb). Not a single paper deals with rhetorical figures except in the most peripheral way. In Computer Science, the situation is little better. The case has been made, dating to the turn of the century, that computational argument studies have a rich potential to investigate figures ([8,9,25,54]), and there is growing success in the detection of rhetorical figures ([12,13,23,24,29,31,50,58]). But, until this volume and the groundbreaking work of Lawrence, Visser, and Reed, these two strains – the detection of the forms and the study of argumentative functions – have not been brought together.
One issue has dogged the entire history of rhetorical figures, and it is one that is particularly relevant for computational detection: when is some expression, x, a ‘real figure,’ and when is it just another arbitrary piece of language? The issue often comes up around notions of literality, often around tropes, and usually in discussions starring metaphor. For instance, such discussions might pick up on the use of dogged in the initial sentence of this section. Is it ‘really’ a metaphor, or just a verb that means something like ‘attend closely’? (Or perhaps both, going into a third category, the dead or dormant metaphor.) While metaphor and tropes more generally are the focus of such discussions, other words and phrases are equally implicated, and they help to show how artificial the whole ‘real figure’ issue is. We don’t ask whether words like rolly poly and hoi polloi ‘really’ rhyme, for instance (word-final syllable repetition), or whether vice versa and ship shape ‘really’ alliterate (word-initial consonant repetition). Rhetoricians, with a larger vocabulary of these patterns, don’t stay up nights wondering if very very or bye-bye are ‘real’ epizeuxes (immediate lexical repetition, with no intervening material), whether easy come, easy go or first come, first served are ‘really’ epanaphoras, whether clichés like we shall see what we shall see or it is what it is are ‘really’ epanalepses. There are processes of familiarity and entrenchment that can make property clumps in some expressions less visible than in others. For semantic properties, which are not actually visible at all, the figural motivations can disappear quickly into history for all but the etymologists. Semantic figural motivations for words and expressions are ground down in the constant friction of linguistic commerce. Those processes, in an attrition of salience, are what give us the perceptual phenomenon we call literality. Formal figural motivations can grind away as well, leading to a translucent blandness that gives us clichés and other routine figurations, but they are as much a part of everyday language as the vaunted semantic motivations.
Because they emerge from our cognitive dispositions, and because they satisfy functional needs, figural patterns arise naturally in all communicative contexts. Lawrence, Visser, & Reed’s contribution to this volume shows that very clearly for the communicative context we know and love as argumentation. For us, therefore, rhetorical figures are not specialty devices reserved for orators, poets, and rap artists. While craft and talent can produce distinctive figural realizations, figures do not require deliberation or design. A figure is its pattern, and no pattern is more authentic than another.
Concerns about authenticity and design, however, lead many researchers to worry about false positives in figure detection [12,13,29,58]. Dubremetz and Nivre, for instance, rank the examples they find on a scale of figural prototypicality ([12]), while Lawrence, Visser, & Reed, in this volume, talk in terms of quasi- and non-instances of figures in their results – things that aren’t ‘really’ figures, just accidentally figure-like. The concerns underlying these designations are genuine, and the algorithmic activity that finds and even ranks these differences in the texts they plumb is very important. We need to know what to call, and how to treat, the pieces of data the algorithms return. But these researchers are not finding and categorizing differences among individual figures.
In our research, we adopt the policy that figures are defined only by their linguistic properties (such as the repetition or the cross-domain mapping of words). Or, in the slogans of our lab, “Figures are what figures are,” and “a ploce is a ploce is a ploce.” If an algorithm comes back with proximal reverse occurrences of at least two words, it has come back with an antimetabole. Period. In fact, what detection researchers are finding is not better or worse versions of given figures, but sites of more or less figural action. Take the following examples, from Dubremetz and Nivre [12]:
A comedian does funny things, a good comedian does things funny. We are all European citizens and we are all citizens of a European Union which is underpinned[, politically speaking, by a basic set of principles].
Dubremetz and Nivre are on the hunt for chiasmi, figures of reverse constituent repetition, and we agree with their intuitions that 18 ‘feels’ more chiastic than 19. We also agree that the notion of prototypicality seems to catch what is triggering our respective intuitions.4
Dubremetz and Nivre ([12–14]) use the term chiasmus to refer to the figure antimetabole, not an uncommon usage in the rhetorical tradition, but we seek more precision in the terminology of rhetorical figures than they (or frankly, most rhetoricians, let alone computer scientists) are interested in. In our work, we have found multiple reverse-constituent-repetition patterns, which we regard as different figures, adapting and coining a variety of labels for them, grouped in our chiastic suite. For instance, their example 18 is chiastic, since it involves reverse repetition of constituents. It is not an antimetabole, however, because those constitutents are not words, but only signantia (words are signans/signatum pairings). We categorize it with the blended neologism antanametabole, since it is a combination of antanaclasis (repetition of the ‘same’ word with different senses) and antimetabole. We have also used the term pseudo-antimetabole for this pattern, since it closely mimics an antimetabole.
Despite the thousands of years that figures have been studied, organized, and theorized, there is considerable research remaining to do into how they combine, and what sorts of functions they serve in specific combinations (the discussion of antithesis-antimetabole combinations in connection with 16, is one such combination). Luckily for us, computers are the best tools for doing such research.
This issue features four rather different papers, organized around the intersection of rhetorical figures and argumentation, all of them with an eye on computational research, all of them major contributions to computational rhetoric. They are nicely split among the disciplines. Two are by rhetoricians, with their emphases on context, historical development, and linguistic nuance. Two feature teams in computer science, with their attention to application, tractability, and testable results.
“Rhetorical figures as argument schemes – The proleptic suite,” by Ashley Rose Mehlenbacher, is an important bridging study that helps integrate the category of figures known as moves with the extensive research in argument schemes. Mehlenbacher works through the distended and terminologically inconsistent scholarship on figures of anticipation and rebuttal, in literature and in argumentation, to reveal a tractable set of such figures, aligning that set with the classical tradition of topoi and its contemporary descendants, argument schemes.
Ying Yuan’s “The argumentative litotes in The analects” finds not quite a suite of figures, but through its careful study of a subtle trope of negation in an ancient Chinese text, alongside Aristotle’s not-quite-as ancient semantic analysis of opposition, finds three distinct subtypes: contradictory, contrary and relative litotes. Among the many virtues of rhetorical figures for the study of argumentation is that their neurocognitive dimensions point strongly to a universalist, cross-cultural approach to the form arguments take. There is perhaps an infinite variety of argument realizations, and languages all have different grooves in which they flow, but since arguments are all the products of human minds engaging other human minds, there are also important patterns of commonality. We see those patterns in language (the presence of nouns and verbs, for instance, and subjects and predicates, and morpholexical-syntactic relations). Figures provide a way to see those patterns of commonality in argumentation. So, a study based in a language like Chinese, with such different grooves from the familiar European languages in which argumentation has been investigated, is always a welcome addition to the field.
“Ontological representations of rhetorical figures for argumentation mining,” an international collaboration by Jelena Mitrović, Cliff O’Reilly Miljana Mladenović, and Siegfried Handschuh, also has cross-linguistic virtues. It surveys several early projects ontologically modelling rhetorical figures, including the most ambitious such project, in Serbian. It also attends seriously to the application of ontologies in the detection of figures, as well as the detection of the semantic relations of Rhetorical Structure Theory, the inferential relations of argument schemes, and other relevant text relations and structures – the necessary first step in using them for argument mining. In connection with this paper in particular, we should note a discrepancy in the use of the word rhetorical in the important text classification and analysis research coming out of Mann and Thompson’s Rhetorical Structure Theory (RST) [44]. As we have noted elsewhere [56], we regard that work as vitally important, and RST more generally as a valuable set of insights into the semantic organization of texts, especially at inter- and intra-clausal levels, but the adjective rhetorical in the name of the theory is gratuitous. The theory, and the research it has sponsored to this point, has no serious connection to the rhetorical tradition at all, as it is understood to date back to the 5th century BCE. RST would be more accurately labelled if it were TST (Textual Structure Theory) or DST (Discourse Structure Theory), or the like. We certainly don’t wish Mann and Thompson now to rename their approach, but we caution readers to be aware that the use of rhetorical is wholly equivocal – between a vague and generalized signifier and a technical, disciplinary term – whenever RST and genuinely rhetorical notions are discussed together. Mitrović and her colleagues are aware of this difference, of course, and work to keep the usages distinct, but the pitfall is there all the same for readers.
All of the papers in this issue bring together rhetorical figures, argument analysis, and computation in productive ways that help set the agenda for this emerging confluence, but the final paper is groundbreaking. In “Harnessing rhetorical figures for argument mining: A pilot study in relating figures of speech to argument structure,” John Lawrence, Jacky Visser, and Chris Reed, all of them affiliated with the Centre for Argument Technology, bring for the first time the form-function alliance of rhetorical figures into serious commerce with computational argument studies. It has been a fruitful decade since Jakub Gawryjolek initiated computational figure-detection work ([23,24]). Important innovations have appeared, both in terms of the detection strategies and the diagnostic deployment. Claus Strommer [58], for instance, makes headway into document intent, finding correlations with pathos (emotional appeal), and James Java reports promising results with a range of figures for authorship attribution ([31]). Daniel Hromada added feature constraints ([29]) for a small set of repetition schemes, while Marie Dubremetz and Jakob Nivre zeroed in on the tricky but rich and fascinating chiastic figures (primarily antimetaboles, though they do not distinguish among them), charting them in terms of prototypicality ([12]), increasing precision by including syntactic structure (augmenting their shallow-feature approach [13]), and have corpus-trained a chiasmus classifier (rather than the previous hand-tuning methods [14]).
But no one before Lawrence, Visser, and Reed has directly investigated the argumentative functions of rhetorical figures computationally. It is a pilot study, but – since we’re talking about rhetorical figures, we hope you’ll forgive the conceit – we think of it more as a pilot light, one that can now ignite two tremendously important and mutually informing lines of research that are nascent in earlier work. The first line tests ancient and modern claims about the general purposes of rhetorical figures, and about the specific purposes of many individual figures; very likely, we are confident, uncovering new purposes as well. Much as computational methods and corpus studies revolutionized lexicography by validating, improving, and replacing aspects of traditional dictionary writing, computational corpus research that finds figures and investigates the surrounding text can renovate the field of rhetoric. The second line more directly implicates argumentation studies, by providing subtle and powerful new tools for argument mining. As empirical rigour grows and results accrue and we build an inventory of rhetorical figures to serve as discourse diagnostics, we can use computational methods to develop a deeper understanding of the cognitive and social dimensions of argumentation, and more robust methods to probe argumentative discourse.
Glossary
The terminology of rhetorical figures, and rhetoric more generally, is arcane and, with the exception of a few terms (such as metaphor and irony) largely unfamiliar beyond the discipline of rhetoric. This glossary includes all the rhetorical terms in the volume, most prominently all of the figures, with examples (in parentheses, with relevant elements highlighted, many of them taken from the articles in this volume). A word about the examples, however: since rhetorical figures very often travel together, a passage that exemplifies one figure will also exemplify others. One needs to map the definitions against the examples to identify the elements that represent the specific figure at hand; and, equally, to disregard the other figures that might be present.
Please note that the terminology of rhetorical figures is quite complex, and many of the figures in our glossary have synonyms, near synonyms, or alternate spellings that might be encountered elsewhere; equally, the definitions you find elsewhere for some of these terms may be at variance with the definitions here. Many terms might refer to the same configuration (an embarrassment of synonyms); conversely, one term might refer to multiple configurations (an embarrassment of negligent referential inconsistency). There is, in short, a pressing need for the standardization of figurative terminology. In this volume we adhere to precise one-term-one-configuration definitions and adopt the four-way taxonomy of figures defined in the Waterloo Rhetorical Figure Ontology [5,26]: schemes, tropes, chroma, and moves. The scheme and trope categories are among the oldest in figuration, and, construed according to a simple signans/signatum division, the most basic and the easiest to see. Schemes are formal deviations, shifts away from conventional expectations in the usage of signantia. Protoypical examples include rhyme and ploce. Tropes are conceptual deviations, shifts away from conventional expectations in the usage of signata. Protoypical examples include metaphor and metonymy. Chroma are deviations from conventional expectations of speaker intention. Protoypical examples include erotema (“rhetorical question”) and sarcasm. Moves are deviations from conventional expectations of discourse patterns. Protoypical examples include anemographia (descriptive passages evoking wind) and paralipsis (pretending to ignore something while giving it salience).
While this glossary no doubt feels exhaustive (if not exhausting) to non-rhetoricians, it is only comprised of the terms used in this special issue, including a relatively small (but highly representative) slice of the available figures. Also, while there is an abundance of technical terminology in this volume, and in any scientific journal, this glossary only concerns itself with rhetorical terms, on the reasonable assumption that the vocabulary of computational linguistics and argumentation theory is familiar, or easily accessible, to our readership.
A synonym for
A synonym for
A
A
A
A synonym for
A
A
A
A synonym for
A
A
A type of
A
A
A synonym for
A
A
A
A type of
A
Often used as a synonym
A group of figures, including
A major category of figures,
Also commutatio
See
A specific figure comprised of other figures, to be distinguished from a passage of text which includes, for various reasons, multiple figures. Only a few specific combinations of figures are compound figures (e.g.
Computationally mediated research into persuasion, credibility, affect, arrangement, topical structure, style (including rhetorical figures), or other notions informed by the rhetorical tradition.
A
A
A synonym for
A syonym for
A
A
A class of
A
A synonym for
A
A
A
A
A
A synonym for
A
A semiotic characteristic of language and other systems of representation such that the form of the signans resembles the signatum in some way, such as in sound, in proportion, or in sequence.
A
A
A
A
A
A
A
A
A
A major category of figures,
Traditionally a synonym for
A syonym for
A type of
A
A
A
A synonym for
A
A
A synonym for
A term from Perelman and Olbrecht-Tyteca’s [52] framework for the salience given to aspects of an argument through linguistic style, frequently correlated with rhetorical figures.
A
See
A set of
Perelman and Olbrecht-Tyteca’s [52] term for arguments that appear demonstrative but do not have the rigour of genuinely (mathematical or logical) demonstrative arguments. They designate several figures as potentially having this characteristic, including
A synonym for
A synonym for
See
Proximal repetition word-final syllables (h
A type of
A major category of figures,
(plural,
(plural,
A
See
A
A
Tools of invention and analysis related to the structure of the argument, many of them related to figures. For example, argument from analogy is related to
A major category of figures,
A class of
Footnotes
Acknowledgements
The authors would like to thank, very gratefully, Devon Moriarty for her painstaking and elegant editorial assistance; as well as Pietro Baroni, Adam James Bradley, Sean Fischer, Michael Flor, Kenneth Graham, Floriana Grasso, Nancy Green, Douglas Guilbeault, Ken Hirschkop, Daniel Hromada, Beata Beigman Klebanov, David Larochelle, Michael Macdonald, Andrew McMurry, Laura Neuhaus, Claus Strommer, Michael Ullyot, Bart Verheij, and James Wynn for their help with this issue; and Sarah Bott, Sebastian Malton, Omar Nafees, Paulo Pacheco, Katherine Tu, Yetian Wang, and the whole RhetFig family, for constant, enervating conversations about figures, computers, and argumentation. The positions, claims, and reasons offered in this introduction were most recently presented to the Artificial Intelligence and Simulated Behaviour workshop on Cognitive Ontologies and at the Computer Models of Natural Argumentation workshop. The authors are very appreciative to the organizers and participants of those workshops for their vigorously constructive feedback.
This research was partially funded by the Social Sciences and Humanities Research Council of Canada.
