c© 2002 Association for Computational Linguistics
Near-Synonymy and Lexical Choice
Philip Edmonds
∗
Graeme Hirst
†
Sharp Laboratories of Europe Limited University of Toronto
We develop a new computational model for representing the fine-grained meanings of near-
synonyms and the differences between them. We also develop a lexical-choice process that can
decide which of several near-synonyms is most appropriate in a particular situation. This research
has direct applications in machine translation and text generation.
We first identify the problems of representing near-synonyms in a computational lexicon
and show that no previous model adequately accounts for near-synonymy. We then propose a
preliminary theory to account for near-synonymy, relying crucially on the notion of granularity
of representation, in which the meaning of a word arises out of a context-dependent combination
of a context-independent core meaning and a set of explicit differences to its near-synonyms. That
is, near-synonyms cluster together.
We then develop a clustered model of lexical knowledge, derived from the conventional on-
tological model. The model cuts off the ontology at a coarse grain, thus avoiding an awkward
proliferation of language-dependent concepts in the ontology, yet maintaining the advantages
of efficient computation and reasoning. The model groups near-synonyms into subconceptual
clusters that are linked to the ontology. A cluster differentiates near-synonyms in terms of fine-
grained aspects of denotation, implication, expressed attitude, and style. The model is general
enough to account for other types of variation, for instance, in collocational behavior.
An efficient, robust, and flexible fine-grained lexical-choice process is a consequence of a
clustered model of lexical knowledge. To make it work, we formalize criteria for lexical choice
as preferences to express certain concepts with varying indirectness, to express attitudes, and
to establish certain styles. The lexical-choice process itself works on two tiers: between clusters
and between near-synonyns of clusters. We describe our prototype implementation of the system,
called I-Saurus.
1. Introduction
A word can express a myriad of implications, connotations, and attitudes in addition
to its basic “dictionary” meaning. And a word often has near-synonyms that differ
from it solely in these nuances of meaning. So, in order to find the right word to
use in any particular situation—the one that precisely conveys the desired meaning
and yet avoids unwanted implications—one must carefully consider the differences
between all of the options. Choosing the right word can be difficult for people, let
alone present-day computer systems.
For example, how can a machine translation (MT) system determine the best En-
glish word for the French b´evue when there are so many possible similar but slightly
∗ Sharp Laboratories of Europe Limited, Oxford Science Park, Edmund Halley Road, Oxford OX4 4GB,
England. E-mail: phil@sharp.co.uk.
† Department of Computer Science, University of Toronto, Ontario, Canada M5S 3G4. E-mail:
gh@cs.toronto.edu.
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Computational Linguistics Volume 28, Number 2
different translations? The system could choose error, mistake, blunder, slip, lapse, boner,
faux pas, boo-boo, and so on, but the most appropriate choice is a function of how b´evue
is used (in context) and of the difference in meaning between b´evue and each of the En-
glish possibilities. Not only must the system determine the nuances that b´evue conveys
in the particular context in which it has been used, but it must also find the English
word (or words) that most closely convey the same nuances in the context of the other
words that it is choosing concurrently. An exact translation is probably impossible, for
b´evue is in all likelihood as different from each of its possible translations as they are
from each other. That is, in general, every translation possibility will omit some nuance
or express some other possibly unwanted nuance. Thus, faithful translation requires
a sophisticated lexical-choice process that can determine which of the near-synonyms
provided by one language for a word in another language is the closest or most
appropriate in any particular situation. More generally, a truly articulate natural lan-
guage generation (NLG) system also requires a sophisticated lexical-choice process.
The system must to be able to reason about the potential effects of every available
option.
Consider, too, the possibility of a new type of thesaurus for a word processor that,
instead of merely presenting the writer with a list of similar words, actually assists
the writer by ranking the options according to their appropriateness in context and
in meeting general preferences set by the writer. Such an intelligent thesaurus would
greatly benefit many writers and would be a definite improvement over the simplistic
thesauri in current word processors.
What is needed is a comprehensive computational model of fine-grained lexical
knowledge. Yet although synonymy is one of the fundamental linguistic phenomena
that influence the structure of the lexicon, it has been given far less attention in lin-
guistics, psychology, lexicography, semantics, and computational linguistics than the
equally fundamental and much-studied polysemy. Whatever the reasons—philosophy,
practicality, or expedience—synonymy has often been thought of as a “non-problem”:
either there are synonyms, but they are completely identical in meaning and hence
easy to deal with, or there are no synonyms, in which case each word can be handled
like any other. But our investigation of near-synonymy shows that it is just as com-
plex a phenomenon as polysemy and that it inherently affects the structure of lexical
knowledge.
The goal of our research has been to develop a computational model of lexical
knowledge that can adequately account for near-synonymy and to deploy such a
model in a computational process that could “choose the right word” in any situa-
tion of language production. Upon surveying current machine translation and natural
language generation systems, we found none that performed this kind of genuine
lexical choice. Although major advances have been made in knowledge-based mod-
els of the lexicon, present systems are concerned more with structural paraphrasing
and a level of semantics allied to syntactic structure. None captures the fine-grained
meanings of, and differences between, near-synonyms, nor the myriad of criteria in-
volved in lexical choice. Indeed, the theories of lexical semantics upon which present-
day systems are based don’t even account for indirect, fuzzy, or context-dependent
meanings, let alone near-synonymy. And frustratingly, no one yet knows how to
implement the theories that do more accurately predict the nature of word mean-
ing (for instance, those in cognitive linguistics) in a computational system (see Hirst
[1995]).
In this article, we present a new model of lexical knowledge that explicitly accounts
for near-synonymy in a computationally implementable manner. The clustered model
of lexical knowledge clusters each set of near-synonyms under a common, coarse-
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Edmonds and Hirst Near-Synonymy and Lexical Choice
grained meaning and provides a mechanism for representing finer-grained aspects of
denotation, attitude, style, and usage that differentiate the near-synonyms in a cluster.
We also present a robust, efficient, and flexible lexical-choice algorithm based on the
approximate matching of lexical representations to input representations. The model
and algorithm are implemented in a sentence-planning system called I-Saurus, and
we give some examples of its operation.
2. Near-Synonymy
2.1 Absolute and Near-Synonymy
Absolute synonymy, if it exists at all, is quite rare. Absolute synonyms would be able
to be substituted one for the other in any context in which their common sense is
denoted with no change to truth value, communicative effect, or “meaning” (however
“meaning” is defined). Philosophers such as Quine (1951) and Goodman (1952) argue
that true synonymy is impossible, because it is impossible to define, and so, perhaps
unintentionally, dismiss all other forms of synonymy. Even if absolute synonymy were
possible, pragmatic and empirical arguments show that it would be very rare. Cruse
(1986, page 270) says that “natural languages abhor absolute synonyms just as nature
abhors a vacuum,” because the meanings of words are constantly changing. More for-
mally, Clark (1992) employs her principle of contrast, that “every two forms contrast
in meaning,” to show that language works to eliminate absolute synonyms. Either an
absolute synonym would fall into disuse or it would take on a new nuance of mean-
ing. At best, absolute synonymy is limited mostly to dialectal variation and technical
terms (underwear (AmE) : pants (BrE); groundhog : woodchuck; distichous : two-ranked; ple-
sionym : near-synonym), but even these words would change the style of an utterance
when intersubstituted.
Usually, words that are close in meaning are near-synonyms (or plesionyms)
1
—
almost synonyms, but not quite; very similar, but not identical, in meaning; not fully
intersubstitutable, but instead varying in their shades of denotation, connotation, im-
plicature, emphasis, or register (DiMarco, Hirst, and Stede 1993).
2
Section 4 gives a
more formal definition.
Indeed, near-synonyms are pervasive in language; examples are easy to find. Lie,
falsehood, untruth, fib, and misrepresentation, for instance, are near-synonyms of one
another. All denote a statement that does not conform to the truth, but they differ
from one another in fine aspects of their denotation. A lie is a deliberate attempt
to deceive that is a flat contradiction of the truth, whereas a misrepresentation may
be more indirect, as by misplacement of emphasis, an untruth might be told merely
out of ignorance, and a fib is deliberate but relatively trivial, possibly told to save
one’s own or another’s face (Gove 1984). The words also differ stylistically; fib is an
informal, childish term, whereas falsehood is quite formal, and untruth can be used
euphemistically to avoid some of the derogatory implications of some of the other
terms (Gove [1984]; compare Coleman and Kay’s [1981] rather different analysis). We
will give many more examples in the discussion below.
1 In some of our earlier papers, we followed Cruse (1986) in using the term plesionym for near-synonym,
the prefix plesio- meaning ‘near’. Here, we opt for the more-transparent terminology. See Section 4 for
discussion of Cruse’s nomenclature.
2 We will not add here to the endless debate on the normative differentiation of the near-synonyms
near-synonym and synonym (Egan 1942; Sparck Jones 1986; Cruse 1986; Church et al. 1994). It is
sufficient for our purposes at this point to simply say that we will be looking at sets of words that are
intuitively very similar in meaning but cannot be intersubstituted in most contexts without changing
some semantic or pragmatic aspect of the message.
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Computational Linguistics Volume 28, Number 2
Error implies a straying from a proper course and suggests guilt as may lie in failure to take
proper advantage of a guide. ... Mistake implies misconception, misunderstanding, a wrong
but not always blameworthy judgment, or inadvertence; it expresses less severe criticism than
error. Blunder is harsher than mistake or error; it commonly implies ignorance or stupidity, some-
times blameworthiness. Slip carries a stronger implication of inadvertence or accident than mis-
take, and often, in addition, connotes triviality. Lapse, though sometimes used interchangeably
with slip, stresses forgetfulness, weakness, or inattention more than accident; thus, one says a
lapse of memory or a slip of the pen, but not vice versa. Faux pas is most frequently applied to
a mistake in etiquette. Bull, howler, and boner are rather informal terms applicable to blunders
that typically have an amusing aspect.
Figure 1
An entry (abridged) from Webster’s New Dictionary of Synonyms (Gove 1984).
2.2 Lexical Resources for Near-Synonymy
It can be difficult even for native speakers of a language to command the differences
between near-synonyms well enough to use them with invariable precision, or to ar-
ticulate those differences even when they are known. Moreover, choosing the wrong
word can convey an unwanted implication. Consequently, lexicographers have com-
piled many reference books (often styled as “dictionaries of synonyms”) that explicitly
discriminate between members of near-synonym groups. Two examples that we will
cite frequently are Webster’s New Dictionary of Synonyms (Gove 1984), which discrimi-
nates among approximately 9,000 words in 1,800 near-synonym groups, and Choose the
Right Word (Hayakawa 1994), which covers approximately 6,000 words in 1,000 groups.
The nuances of meaning that these books adduce in their entries are generally much
more subtle and fine-grained than those of standard dictionary definitions. Figure 1
shows a typical entry from Webster’s New Dictionary of Synonyms, which we will use as
a running example. Similar reference works include Bailly (1970), B´enac (1956), Fer-
nald (1947), Fujiwara, Isogai, and Muroyama (1985), Room (1985), and Urdang (1992),
and usage notes in dictionaries often serve a similar purpose. Throughout this article,
examples that we give of near-synonyms and their differences are taken from these
references.
The concept of difference is central to any discussion of near-synonyms, for if two
putative absolute synonyms aren’t actually identical, then there must be something
that makes them different. For Saussure (1916, page 114), difference is fundamental to
the creation and demarcation of meaning:
In a given language, all the words which express neighboring ideas help define
one another’s meaning. Each of a set of synonyms like redouter (‘to dread’),
craindre (‘to fear’), avoir peur (‘to be afraid’) has its particular value only because
they stand in contrast with one another.... No word has a value that can be
identified independently of what else is in its vicinity.
There is often remarkable complexity in the differences between near-synonyms.
3
Consider again Figure 1. The near-synonyms in the entry differ not only in the ex-
pression of various concepts and ideas, such as misconception and blameworthiness,
but also in the manner in which the concepts are conveyed (e.g., implied, suggested,
3 This contrasts with Markman and Gentner’s work on similarity (Markman and Gentner 1993; Gentner
and Markman 1994), which suggests that the more similar two items are, the easier it is to represent
their differences.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
Table 1
Examples of near-synonymic variation.
Type of variation Example
Abstract dimension seep : drip
Emphasis enemy : foe
Denotational, indirect error : mistake
Denotational, fuzzy woods : forest
Stylistic, formality pissed : drunk : inebriated
Stylistic, force ruin : annihilate
Expressed attitude skinny : thin : slim, slender
Emotive daddy : dad : father
Collocational task : job
Selectional pass away : die
Subcategorization give : donate
expressed, connoted, and stressed), in the frequency with which they are conveyed
(e.g., commonly, sometimes, not always), and in the degree to which they are conveyed
(e.g., in strength).
2.3 Dimensions of Variation
The previous example illustrates merely one broad type of variation, denotational
variation. In general, near-synonyms can differ with respect to any aspect of their
meaning (Cruse 1986):
• denotational variations, in a broad sense, including propositional, fuzzy,
and other peripheral aspects
• stylistic variations, including dialect and register
• expressive variations, including emotive and attitudinal aspects
• structural variations, including collocational, selectional, and syntactic
variations
Building on an earlier analysis by DiMarco, Hirst, and Stede (1993) of the types of
differentiae used in synonym discrimination dictionaries, Edmonds (1999) classifies
near-synonymic variation into 35 subcategories within the four broad categories above.
Table 1 gives a number of examples, grouped into the four broad categories above,
which we will now discuss.
2.3.1 Denotational Variations. Several kinds of variation involve denotation, taken in
a broad sense.
4
DiMarco, Hirst, and Stede (1993) found that whereas some differen-
tiae are easily expressed in terms of clear-cut abstract (or symbolic) features such as
4 The classic opposition of denotation and connotation is not precise enough for our needs here. The
denotation of a word is its literal, explicit, and context-independent meaning, whereas its connotation
is any aspect that is not denotational, including ideas that color its meaning, emotions, expressed
attitudes, implications, tone, and style. Connotation is simply too broad and ambiguous a term. It often
seems to be used simply to refer to any aspect of word meaning that we don’t yet understand well
enough to formalize.
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Computational Linguistics Volume 28, Number 2
continuous/intermittent (Wine {seeped | dripped} from the barrel), many are not. In fact,
denotational variation involves mostly differences that lie not in simple features but
in full-fledged concepts or ideas—differences in concepts that relate roles and aspects
of a situation. For example, in Figure 1, “severe criticism” is a complex concept that
involves both a criticizer and a criticized, the one who made the error. Moreover, two
words can differ in the manner in which they convey a concept. Enemy and foe, for
instance, differ in the emphasis that they place on the concepts that compose them,
the former stressing antagonism and the latter active warfare rather than emotional
reaction (Gove 1984).
Other words convey meaning indirectly by mere suggestion or implication. There
is a continuum of indirectness from suggestion to implication to denotation; thus slip
“carries a stronger implication of inadvertence” than mistake. Such indirect meanings
are usually peripheral to the main meaning conveyed by an expression, and it is usu-
ally difficult to ascertain definitively whether or not they were even intended to be
conveyed by the speaker; thus error merely “suggests guilt” and a mistake is “not al-
ways blameworthy.” Differences in denotation can also be fuzzy, rather than clear-cut.
The difference between woods and forest is a complex combination of size, primitive-
ness, proximity to civilization, and wildness.
5
2.3.2 Stylistic Variations. Stylistic variation involves differences in a relatively small,
finite set of dimensions on which all words can be compared. Many stylistic dimen-
sions have been proposed by Hovy (1988), Nirenburg and Defrise (1992), Stede (1993),
and others. Table 1 illustrates two of the most common dimensions: inebriated is formal
whereas pissed is informal; annihilate is a more forceful way of saying ruin.
2.3.3 Expressive Variations. Many near-synonyms differ in their marking as to the
speaker’s attitude to their denotation: good thing or bad thing. Thus the same person
might be described as skinny, if the speaker wanted to be deprecating or pejorative,
slim or slender, if he wanted to be more complimentary, or thin if he wished to be
neutral. A hindrance might be described as an obstacle or a challenge, depending upon
how depressed or inspired the speaker felt about the action that it necessitated.
6
A
word can also indirectly express the emotions of the speaker in a possibly finite set
of emotive “fields”; daddy expresses a stronger feeling of intimacy than dad or father.
Some words are explicitly marked as slurs; a slur is a word naming a group of people,
the use of which implies hatred or contempt of the group and its members simply by
virtue of its being marked as a slur.
2.3.4 Structural Variations. The last class of variations among near-synonyms involves
restrictions upon deployment that come from other elements of the utterance and, re-
ciprocally, restrictions that they place upon the deployment of other elements. In either
case, the restrictions are independent of the meanings of the words themselves.
7
The
5 “A ‘wood’ is smaller than a ‘forest’, is not so primitive, and is usually nearer to civilization. This
means that a ‘forest’ is fairly extensive, is to some extent wild, and on the whole not near large towns
or cities. In addition, a ‘forest’ often has game or wild animals in it, which a ‘wood’ does not, apart
from the standard quota of regular rural denizens such as rabbits, foxes and birds of various kinds”
(Room 1985, page 270).
6 Or, in popular psychology, the choice of word may determine the attitude: “[Always] substitute
challenge or opportunity for problem. ... Instead of saying I’m afraid that’s going to be a problem, say That
sounds like a challenging opportunity” (Walther 1992, page 36).
7 It could be argued that words that differ only in these ways should count not merely as near-synonyms
but as absolute synonyms.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
restrictions may be either collocational, syntactic, or selectional—that is, dependent ei-
ther upon other words or constituents in the utterance or upon other concepts denoted.
Collocational variation involves the words or concepts with which a word can be
combined, possibly idiomatically. For example, task and job differ in their collocational
patterns: one can face a daunting task but not ∗face a daunting job. This is a lexical restric-
tion, whereas in selectional restrictions (or preferences) the class of acceptable objects
is defined semantically, not lexically. For example, unlike die, pass away may be used
only of people (or anthropomorphized pets), not plants or animals: ∗Many cattle passed
away in the drought.
Variation in syntactic restrictions arises from differing syntactic subcategorization.
It is implicit that if a set of words are synonyms or near-synonyms, then they are
of the same syntactic category.
8
Some of a set of near-synonyms, however, might be
subcategorized differently from others. For example, the adjective ajar may be used
predicatively, not attributively (The door is ajar; ∗the ajar door), whereas the adjective
open may be used in either position. Similarly, verb near-synonyms (and their nomi-
nalizations) may differ in their verb class and in the alternations that they they may
undergo (Levin 1993). For example, give takes the dative alternation, whereas donate
does not: Nadia gave the Van Gogh to the museum; Nadia gave the museum the Van Gogh;
Nadia donated the Van Gogh to the museum; ∗Nadia donated the museum the Van Gogh.
Unlike the other kinds of variation, collocational, syntactic, and selectional varia-
tions have often been treated in the literature on lexical choice, and so we will have
little more to say about them here.
2.4 Cross-Linguistic Near-Synonymy
Near-synonymy rather than synonymy is the norm in lexical transfer in translation:
the word in the target language that is closest to that in the source text might be a
near-synonym rather than an exact synonym. For example, the German word Wald is
similar in meaning to the English word forest, but Wald can denote a rather smaller
and more urban area of trees than forest can; that is, Wald takes in some of the English
word woods as well, and in some situations, woods will be a better translation of Wald
than forest. Similarly, the German Geh¨olz takes in the English copse and the “smaller”
part of woods. We can think of Wald, Geh¨olz, forest, woods, and copse as a cross-linguistic
near-synonym group.
Hence, as with a group of near-synonyms from a single language, we can speak
of the differences in a group of cross-linguistic near-synonyms. And just as there are
reference books to advise on the near-synonym groups of a single language, there
are also books to advise translators and advanced learners of a second language on
cross-linguistic near-synonymy. As an example, we show in Figures 2 and 3 (abridge-
ments of) the entries in Farrell (1977) and Batchelor and Offord (1993) that explicate,
from the perspective of translation to and from English, the German and French near-
synonym clusters that correspond to the English cluster for error that we showed in
Figure 1.
2.5 Summary
We know that near-synonyms can often be intersubstituted with no apparent change
of effect on a particular utterance, but, unfortunately, the context-dependent nature
8 A rigorous justification of this point would run to many pages, especially for near-synonyms. For
example, it would have to be argued that the verb sleep and the adjective asleep are not merely
near-synonyms that just happen to differ in their syntactic categories, even though the sentences Emily
sleeps and Emily is asleep are synonymous or nearly so.
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Computational Linguistics Volume 28, Number 2
MISTAKE, ERROR. Fehler is a definite imperfection in a thing which ought not to be there. In
this sense, it translates both mistake and error. Irrtum corresponds to mistake only in the sense of
‘misunderstanding’, ‘misconception’, ‘mistaken judgment’, i.e. which is confined to the mind,
not embodied in something done or made. [footnote:] Versehen is a petty mistake, an oversight,
a slip due to inadvertence. Mißgriff and Fehlgriff are mistakes in doing a thing as the result
of an error in judgment.
Figure 2
An entry (abridged) from Dictionary of German Synonyms (Farrell 1977).
impair (3) blunder, error
b´evue (3–2) blunder (due to carelessness or ignorance)
faux pas (3–2) mistake, error (which affects a person adversely socially or in his/her career,
etc)
bavure (2) unfortunate error (often committed by the police)
bˆetise (2) stupid error, stupid words
gaffe (2–1) boob, clanger
Figure 3
An entry (abridged) from Using French Synonyms (Batchelor and Offord 1993). The
parenthesized numbers represent formality level from 3 (most formal)to1(least formal).
of lexical knowledge is not very well understood as yet. Lexicographers, for in-
stance, whose job it is to categorize different uses of a word depending on context,
resort to using mere “frequency” terms such as sometimes and usually (as in Fig-
ure 1). Thus, we cannot yet make any claims about the influence of context on near-
synonymy.
In summary, to account for near-synonymy, a model of lexical knowledge will
have to incorporate solutions to the following problems:
• The four main types of variation are qualitatively different, so each must
be separately modeled.
• Near-synonyms differ in the manner in which they convey concepts,
either with emphasis or indirectness (e.g., through mere suggestion
rather than denotation).
• Meanings, and hence differences among them, can be fuzzy.
• Differences can be multidimensional. Only for clarity in our above
explication of the dimensions of variation did we try to select examples
that highlighted a single dimension. However, as Figure 1 shows, blunder
and mistake, for example, actually differ on several denotational
dimensions as well as on stylistic and attitudinal dimensions.
• Differences are not just between simple features but involve concepts
that relate roles and aspects of the situation.
• Differences often depend on the context.
3. Near-Synonymy in Computational Models of the Lexicon
Clearly, near-synonymy raises questions about fine-grained lexical knowledge repre-
sentation. But is near-synonymy a phenomenon in its own right warranting its own
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Edmonds and Hirst Near-Synonymy and Lexical Choice
legs=2
Bird
elegant
Peacock
gray
elegant
Junco
legs=4
smart
Dog
legs=0,2,4
live-bearing
Mammal
elegant
legs=4
Cat
legs=2
smart
Human
blue-green
egg-laying
Animal
Pfau
juncocat
puss
Katze
Mieze
peacock
bird
Hund
spuglet
animal
human
person
Mensch
Person
Tier
Säugetier
mammal
hound
dog
Junko
Vogel
Figure 4
A simplistic hierarchy of conceptual schemata with connections to their lexical entries for
English and German.
special account, or does it suffice to treat near-synonyms the same as widely differing
words? We will argue now that near-synonymy is indeed a separately characterizable
phenomenon of word meaning.
Current models of lexical knowledge used in computational systems, which are
based on decompositional and relational theories of word meaning (Katz and Fodor
1963; Jackendoff 1990; Lyons 1977; Nirenburg and Defrise 1992; Lehrer and Kittay 1992;
Evens 1988; Cruse 1986), cannot account for the properties of near-synonyms. In these
models, the typical view of the relationship between words and concepts is that each
element of the lexicon is represented as a conceptual schema or a structure of such
schemata. Each word sense is linked to the schema or the conceptual structure that it
lexicalizes. If two or more words denote the same schema or structure, all of them are
connected to it; if a word is ambiguous, subentries for its different senses are connected
to their respective schemata. In this view, then, to understand a word in a sentence
is to find the schema or schemata to which it is attached, disambiguate if necessary,
and add the result to the output structure that is being built to represent the sentence.
Conversely, to choose a word when producing an utterance from a conceptual structure
is to find a suitable set of words that “cover” the structure and assemble them into a
sentence in accordance with the syntactic and pragmatic rules of the language (Nogier
and Zock 1992; Stede 1999).
A conceptual schema in models of this type is generally assumed to contain a
set of attributes or attribute–value pairs that represent the content of the concept and
differentiate it from other concepts. An attribute is itself a concept, as is its value. The
conceptual schemata are themselves organized into an inheritance hierarchy, taxon-
omy, or ontology; often, the ontology is language-independent, or at least language-
neutral, so that it can be used in multilingual applications. Thus, the model might look
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Computational Linguistics Volume 28, Number 2
fib
untruth
mensonge
menterie
misrepresentation
contrevérité
lie
Untrue-Assertion
Accidental-Untruth
Accidental-Contrary-Untruth
Indirect-Deliberate-Untruth
Direct-Deliberate-Untruth
Deliberate-Untruth
Small-Joking-Untruth
Small-Face-Saving-Deliberate-Untruth
Figure 5
One possible hierarchy for the various English and French words for untrue assertions.
Adapted from Hirst (1995).
like the simplified fragment shown in Figure 4. In the figure, the rectangles represent
concept schemata with attributes; the arrows between them represent inheritance. The
ovals represent lexical entries in English and German; the dotted lines represent their
connection to the concept schemata.
9
Following Frege’s (1892) or Tarski’s (1944) truth-conditional semantics, the concept
that a lexical item denotes in such models can be thought of as a set of features that are
individually necessary and collectively sufficient to define the concept. Such a view
greatly simplifies the word–concept link. In a text generation system, for instance, the
features amount to the necessary applicability conditions of a word; that is, they have
to be present in the input in order for the word to be chosen. Although such models
have been successful in computational systems, they are rarely pushed to represent
near-synonyms. (The work of Barnett, Mani, and Rich [1994] is a notable exception;
they define a relation of semantic closeness for comparing the denotations of words
and expressions; see Section 9.) They do not lend themselves well to the kind of fine-
grained and often fuzzy differentiation that we showed earlier to be found in near-
synonymy, because, in these models, except as required by homonymy and absolute
synonymy, there is no actual distinction between a word and a concept: each member
of a group of near-synonyms must be represented as a separate concept schema (or
group of schemata) with distinct attributes or attribute values. For example, Figure 5
shows one particular classification of the fib group of near-synonyms in English and
French.
10
A similar proliferation of concepts would be required for various error clusters
(as shown earlier in Figures 1, 2, and 3).
9 This outline is intended as a syncretism of many models found in the interdisciplinary literature and is
not necessarily faithful to any particular one. For examples, see the papers in Evens (1988) (especially
Sowa [1988]) and in Pustejovsky and Bergler (1992) (especially Nirenburg and Levin [1992], Sowa
[1992], and Burkert and Forster [1992]); for a theory of lexico-semantic taxonomies, see Kay (1971). For
a detailed construction of the fundamental ideas, see Barsalou (1992); although we use the term schema
instead of frame, despite Barsalou’s advice to the contrary, we tacitly accept most elements of his
model. For bilingual aspects, see Kroll and de Groot (1997).
10 We do not claim that a bilingual speaker necessarily stores words and meanings from different
languages together. In this model, if the concepts are taken to be language independent, then it does
not matter if one overarching hierarchy or many distinct hierarchies are used. It is clear, however, that
cross-linguistic near-synonyms do not have exactly the same meanings and so require distinct concepts
in this model.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
Although some systems have indeed taken this approach (Emele et al. 1992), this
kind of fragmentation is neither easy nor natural nor parsimonious. Hirst (1995) shows
that even simple cases lead to a multiplicity of nearly identical concepts, thereby
defeating the purpose of a language-independent ontology. Such a taxonomy cannot
efficiently represent the multidimensional nature of near-synonymic variation, nor can
it account for fuzzy differences between near-synonyms. And since the model defines
words in terms of only necessary and sufficient truth-conditions, it cannot account
for indirect expressions of meaning and for context-dependent meanings, which are
clearly not necessary features of a word’s meaning.
Moreover, a taxonomic hierarchy emphasizes hyponymy, backgrounding all other
relations, which appear to be more important in representing the multidimensional
nature of fine-grained word meaning. It is not even clear that a group of synonyms
can be structured by hyponymy, except trivially (and ineffectively) as hyponyms all of
the same concept.
The model also cannot easily or tractably account for fuzzy differences or the full-
fledged concepts required for representing denotational variation. First-order logic,
rather than the description logic generally used in ontological models, would at least
be required to represent such concepts, but reasoning about the concepts in lexical
choice and other tasks would then become intractable as the model was scaled up to
represent all near-synonyms.
In summary, present-day models of the lexicon have three kinds of problems with
respect to near-synonymy and fine-grained lexical knowledge: the adequacy of cov-
erage of phenomena related to near-synonymy; engineering, both in the design of
an efficient and robust lexical choice process and in the design of lexical entries for
near-synonyms; and the well-known issues of tractability of reasoning about concepts
during natural language understanding and generation.
Nevertheless, at a coarse grain, the ontological model does have practical and the-
oretical advantages in efficient paraphrasing, lexical choice, and mechanisms for infer-
ence and reasoning. Hence, to build a new model of lexical knowledge that takes into
account the fine-grainedness of near-synonymy, a logical way forward is to start with
the computationally proven ontological model and to modify or extend it to account
for near-synonymy. The new model that we will present below will rely on a much
more coarsely grained ontology. Rather than proliferating conceptual schemata to ac-
count for differences between near-synonyms, we will propose that near-synonyms
are connected to a single concept, despite their differences in meaning, and are dif-
ferentiated at a subconceptual level. In other words, the connection of two or more
words to the same schema will not imply synonymy but only near-synonymy. Dif-
ferentiation between the near-synonyms—the fine tuning—will be done in the lexical
entries themselves.
4. Near-Synonymy and Granularity of Representation
To introduce the notion of granularity to our discussion, we first return to the problem
of defining near-synonymy.
Semanticists such as Ullmann (1962), Cruse (1986), and Lyons (1995) have at-
tempted to define near-synonymy by focusing on “propositional” meaning. Cruse, for
example, contrasts cognitive synonyms and plesionyms; the former are words that,
when intersubstituted in a sentence, preserve its truth conditions but may change the
expressive meaning, style, or register of the sentence or may involve different idiosyn-
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Computational Linguistics Volume 28, Number 2
cratic collocations (e.g., violin : fiddle),
11
whereas intersubstituting the latter changes
the truth conditions but still yields semantically similar sentences (e.g., misty : foggy).
Although these definitions are important for truth-conditional semantics, they are not
very helpful for us, because plesionymy is left to handle all of the most interesting
phenomena discussed in Section 2. Moreover, a rigorous definition of cognitive syn-
onymy is difficult to come up with, because it relies on the notion of granularity, which
we will discuss below.
Lexicographers, on the other hand, have always treated synonymy as near-
synonymy. They define synonymy in terms of likeness of meaning, disagreeing only
in how broad the definition ought to be. For instance, Roget followed the vague prin-
ciple of “the grouping of words according to ideas” (Chapman 1992, page xiv). And
in the hierarchical structure of Roget’s Thesaurus, word senses are ultimately grouped
according to proximity of meaning: “the sequence of terms within a paragraph, far
from being random, is determined by close, semantic relationships” (page xiii). The
lexicographers of Webster’s New Dictionary of Synonyms define a synonym as “one of
two or more words ... which have the same or very nearly the same essential mean-
ing....Synonyms can be defined in the same terms up to a certain point” (Egan 1942,
pages 24a–25a). Webster’s Collegiate Thesaurus uses a similar definition that involves the
sharing of elementary meanings, which are “discrete objective denotations uncolored
by ... peripheral aspects such as connotations, implications, or quirks of idiomatic
usage” (Kay 1988, page 9a). Clearly, the main point of these definitions is that near-
synonyms must have the same essential meaning but may differ in peripheral or
subordinate ideas. Cruse (1986, page 267) actually refines this idea and suggests that
synonyms (of all types) are words that are identical in “central semantic traits” and
differ, if at all, only in “peripheral traits.” But how can we specify formally just how
much similarity of central traits and dissimilarity of peripheral traits is allowed? That
is, just what counts as a central trait and what as a peripheral trait in defining a word?
To answer this question, we introduce the idea of granularity of representation
of word meaning. By granularity we mean the level of detail used to describe or
represent the meanings of a word. A fine-grained representation can encode subtle
distinctions, whereas a coarse-grained representation is crude and glosses over vari-
ation. Granularity is distinct from specificity, which is a property of concepts rather
than representations of concepts. For example, a rather general (unspecific) concept,
say Human, could have, in a particular system, a very fine-grained representation, in-
volving, say, a detailed description of the appearance of a human, references to related
concepts such as Eat and Procreate, and information to distinguish the concept from
other similar concepts such as Animal. Conversely, a very specific concept could have
a very coarse-grained representation, using only very general concepts; we could rep-
resent a Lexicographer at such a coarse level of detail as to say no more than that it
is a physical object.
Near-synonyms can occur at any level of specificity, but crucially it is the fine
granularity of the representations of their meanings that enables one to distinguish
one near-synonym from another. Thus, any definition of near-synonymy that does not
take granularity into account is insufficient. For example, consider Cruse’s cognitive
synonymy, discussed above. On the one hand, at an absurdly coarse grain of rep-
resentation, any two words are cognitive synonyms (because every word denotes a
“thing”). But on the other hand, no two words could ever be known to be cognitive
synonyms, because, even at a fine grain, apparent cognitive synonyms might be fur-
11 What’s the difference between a violin and a fiddle? No one minds if you spill beer on a fiddle.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
ther distinguishable by a still more fine-grained representation. Thus, granularity is
essential to the concept of cognitive synonymy, as which pairs of words are cognitive
synonyms depends on the granularity with which we represent their propositional
meanings. The same is true of Cruse’s plesionyms. So in the end, it should not be
necessary to make a formal distinction between cognitive synonyms and plesionyms.
Both kinds of near-synonyms should be representable in the same formalism.
By taking granularity into account, we can create a much more useful definition
of near-synonymy, because we can now characterize the difference between essential
and peripheral aspects of meaning. If we can set an appropriate level of granularity,
the essential meaning of a word is the portion of its meaning that is representable
only above that level of granularity, and peripheral meanings are those portions rep-
resentable only below that level.
But what is the appropriate level of granularity, the dividing line between coarse-
grained and fine-grained representations? We could simply use our intuition—or
rather, the intuitions of lexicographers, which are filtered by some amount of ob-
jectivity and experience. Alternatively, from a concern for the representation of lexical
knowledge in a multilingual application, we can view words as (language-specific)
specializations of language-independent concepts. Given a hierarchical organization
of coarse-grained language-independent concepts, a set of near-synonyms is simply a
set of words that all link to the same language-independent concept (DiMarco, Hirst,
and Stede 1993; Hirst 1995). So in this view, near-synonyms share the same proposi-
tional meaning just up to the point in granularity defined by language dependence.
Thus we have an operational definition of near-synonymy: If the same concept has
several reasonable lexicalizations in different languages, then it is a good candidate for
being considered a language-independent concept, its various lexicalizations forming
sets of near-synonyms in each language.
12
Granularity also explains why it is more difficult to represent near-synonyms in a
lexicon. Near-synonyms are so close in meaning, sharing all essential coarse-grained
aspects, that they differ, by definition, in only aspects representable at a fine grain.
And these fine-grained representations of differences tend to involve very specific
concepts, typically requiring complex structures of more general concepts that are
difficult to represent and to reason with. The matter is only made more complicated
by there often being several interrelated near-synonyms with interrelated differences.
On the other hand, words that are not near-synonyms—those that are merely similar in
meaning (dog : cat) or not similar at all (dog : hat)—could presumably be differentiated
by concepts at a coarse-grained, and less complex, level of representation.
5. A Model of Fine-Grained Lexical Knowledge
Our discussion of granularity leads us to a new model of lexical knowledge in which
near-synonymy is handled on a separate level of representation from coarse-grained
concepts.
5.1 Outline of the Model
Our model is based on the contention that the meaning of an open-class content word,
however it manifests itself in text or speech, arises out of a context-dependent combina-
tion of a basic inherent context-independent denotation and a set of explicit differences
12 EuroWordNet’s Inter-Lingual-Index (Vossen 1998) links the synsets of different languages in such a
manner, and Resnik and Yarowsky (1999) describe a related notion for defining word senses
cross-lingually.
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Computational Linguistics Volume 28, Number 2
to its near-synonyms. (We don’t rule out other elements in the combination, but these
are the main two.) Thus, word meaning is not explicitly represented in the lexicon but
is created (or generated, as in a generative model of the lexicon [Pustejovsky 1995])
when a word is used. This theory preserves some aspects of the classical theories—the
basic denotation can be modeled by an ontology—but the rest of a word’s meaning
relies on other nearby words and the context of use (cf. Saussure). In particular, each
word and its near synonyms form a cluster.
13
The theory is built on the following three ideas, which follow from our observa-
tions about near-synonymy. First, the meaning of any word, at some level of gran-
ularity, must indeed have some inherent context-independent denotational aspect to
it—otherwise, it would not be possible to define or “understand” a word in isolation
of context, as one in fact can (as in dictionaries). Second, nuances of meaning, although
difficult or impossible to represent in positive, absolute, and context-independent
terms, can be represented as differences, in Saussure’s sense, between near-synonyms.
That is, every nuance of meaning that a word might have can be thought of as a rela-
tion between the word and one or more of its near-synonyms. And third, differences
must be represented not by simple features or truth conditions, but by structures that
encode relations to the context, fuzziness, and degrees of necessity.
For example, the word forest denotes a geographical tract of trees at a coarse
grain, but it is only in relation to woods, copse, and other near-synonyms that one
can fully understand the significance of forest (i.e., that it is larger, wilder, etc.). The
word mistake denotes any sort of action that deviates from what is correct and also
involves some notion of criticism, but it is only in relation to error and blunder that
one sees that the word can be used to criticize less severely than these alternatives
allow. None of these differences could be represented in absolute terms, because that
would require defining some absolute notion of size, wildness, or severity, which
seems implausible. So, at a fine grain, and only at a fine grain, we make explicit
use of Saussure’s notion of contrast in demarcating the meanings of near-synonyms.
Hence, the theory holds that near-synonyms are explicitly related to each other not
at a conceptual level but at a subconceptual level—outside of the (coarser-grained)
ontology. In this way, a cluster of near-synonyms is not a mere list of synonyms; it
has an internal structure that encodes fine-grained meaning as differences between
lexical entries, and it is situated between a conceptual model (i.e., the ontology) and
a linguistic model.
Thus the model has three levels of representation. Current computational theo-
ries suggest that at least two levels of representation, a conceptual–semantic level
and a syntactic–semantic level, are necessary to account for various lexico-semantic
phenomena in computational systems, including compositional phenomena such as
paraphrasing (see, for instance, Stede’s [1999] model). To account for fine-grained
meanings and near-synonymy, we postulate a third, intermediate level (or a splitting
of the conceptual–semantic level). Thus the three levels are the following:
A conceptual–semantic level.
|
A subconceptual/stylistic–semantic level.
|
A syntactic–semantic level.
13 It is very probable that many near-synonym clusters of a language could be discovered automatically
by applying statistical techniques, such as cluster analysis, on large text corpora. For instance, Church
et al. (1994) give some results in this area.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
French
German
English
décréter
sommer
soul
English
individual
enjoindre
article
object
entity
thing
item
English
commander
ordonner
blunder
mistake
erreur
someone
faute
mortal
faux pas
bavure
slip
bévue
error
Fehler
Schnitzer
Mißgriff
person
Versehen
bêtise
impair
French
howler
lapse
Irrtum
human
Generic-Order
English
bid
direct
enjoin
command
order
Object
Thing
Generic-Error
PersonActivity
Situation
Figure 6
A clustered model of lexical knowledge
So, taking the conventional ontological model as a starting point, we cut off the
ontology at a coarse grain and cluster near-synonyms under their shared concepts
rather than linking each word to a separate concept. The resulting model is a clus-
tered model of lexical knowledge. On the conceptual–semantic level, a cluster has
a core denotation that represents the essential shared denotational meaning of its
near-synonyms. On the subconceptual/stylistic–semantic level, we represent the fine-
grained differences between the near-synonyms of a cluster in denotation, style, and
expression. At the syntactic–semantic level, syntactic frames and collocational relations
represent how words can be combined with others to form sentences.
Figure 6 depicts a fragment of the clustered model. It shows how the clusters of
the near-synonyms of error, order, person, and object in several languages could be rep-
resented in this model. In the figure, each set of near-synonyms forms a cluster linked
to a coarse-grained concept defined in the ontology: Generic-Error, Generic-Order,
Person, and Object, respectively. Thus, the core denotation of each cluster is the con-
cept to which it points. Within each cluster, the near-synonyms are differentiated at the
subconceptual/stylistic level of semantics, as indicated by dashed lines between the
words in the cluster. (The actual differences are not shown in the figure.) The dashed
lines between the clusters for each language indicate similar cross-linguistic differenti-
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Computational Linguistics Volume 28, Number 2
error
blunder
CAUSE-OF
CORE
ATTRIBUTE
ACTOR
ATTRIBUTE
DEGREE
ACTOR
ATTRIBUTE
Pejorative
Misconception
Stupidity
Concreteness
high
low
Blameworthiness
medium
low high
Activity
Person Deviation
Figure 7
The core denotation and some of the peripheral concepts of the cluster of error nouns. The two
large regions, bounded by the solid line and the dashed line, show the concepts (and attitudes
and styles) that can be conveyed by the words error and blunder in relation to each other.
ation between some or all of the words of each cluster. Not all words in a cluster need
be differentiated, and each cluster in each language could have its own “vocabulary”
for differentiating its near-synonyms, though in practice one would expect an overlap
in vocabulary. The figure does not show the representation at the syntactic–semantic
level. We can now describe the internal structure of a cluster in more detail, starting
with two examples.
Figure 7 depicts part of the representation of the cluster of error nouns (error,
mistake, blunder, ...); it is explicitly based on the entry from Webster’s New Dictionary
of Synonyms shown in Figure 1. The core denotation, the shaded region, represents
an activity by a person (the actor) that is a deviation from a proper course.
14
In the
model, peripheral concepts are used to represent the denotational distinctions of near-
synonyms. The figure shows three peripheral concepts linked to the core concept:
Stupidity, Blameworthiness, and Misconception. The peripheral concepts represent
that a word in the cluster can potentially express, in relation to its near-synonyms,
the stupidity of the actor of the error, the blameworthiness of the actor (of different
degrees: low, medium,orhigh), and misconception as cause of the error. The repre-
sentation also contains an expressed attitude, Pejorative, and the stylistic dimension
of Concreteness. (Concepts are depicted as regular rectangles, whereas stylistic di-
mensions and attitudes are depicted as rounded rectangles.) The core denotation and
peripheral concepts together form a directed graph of concepts linked by relations;
14 Specifiying the details of an actual cluster should be left to trained knowledge representation experts,
who have a job not unlike a lexicographer’s. Our model is intended to encode such knowledge once it
is elucidated.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
CORE
enjoin order
DEGREE
ATTRIBUTE
low highmedium
ACTEE
ACTOR
ATTRIBUTE
highlow medium
ATTRIBUTE
ACTOR
ACTEE
SAYER
SAYING
SAYEE
ACTOR
Person
Authority
Official
ActivityPerson
Communicate
Peremptory
Warn
Formality
Perform
Imperative
Figure 8
The core denotation and peripheral concepts of the cluster of order verbs. The two large
regions, bounded by the solid line and the dashed line, show the concepts that can be
conveyed by the words order and enjoin in relation to each other.
the individual concepts and relations are defined in the ontology. But although all
of the near-synonyms in the cluster will convey the concepts in the core denotation,
the peripheral concepts that will be conveyed depend on each near-synonym. This is
depicted by the two large regions in the figure (bounded by the solid line and the
dashed line), which each contain the concepts, styles, and attitudes conveyed by their
associated near-synonyms, blunder and error, respectively. Thus, error conveys a degree
of Blameworthiness compared to the higher degree that blunder conveys; error does
not convey Stupidity whereas blunder does; blunder can also express a Pejorative
attitude toward the actor, but error does not express any attitude; and error and blunder
differ stylistically in their degree of Concreteness. Notice that the attitude connects
to the concept Person, because all attitudes must be directed toward some entity in
the situation. Stylistic dimensions such as Concreteness, on the other hand, are com-
pletely separate from the graph of concepts. Also, the indirectness of expression of
each of the peripheral concepts by each of the near-synonyms is not shown in this di-
agram (but see below). The Appendix gives the complete representation of this cluster
in the formalism of our model.
Similarly, Figure 8 depicts the cluster of order verbs (order, enjoin, command, ...),
including three of its peripheral concepts and one stylistic dimension. In this cluster,
the core represents a communication by a person (the sayer) to another person (the
sayee) of an activity that the sayee must perform. The core includes several concepts
that are not actually lexicalized by any of the words in the cluster (e.g., the sayer of the
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Computational Linguistics Volume 28, Number 2
order) but that nevertheless have to be represented because the peripheral concepts
refer to them. (Such concepts are indicated by dashed rectangles.) The peripheral
concepts represent the idea that a near-synonym can express the authority of the
sayer (with possible values of Official or Peremptory), a warning to the sayee, and
the imperativeness of the activity (with possible values of low, medium,orhigh). The
figure shows the difference between order (the region bounded by the solid line) and
enjoin (the region bounded by the dashed line).
5.2 Core Denotation
The core denotation of a cluster is the inherent context-independent (and in this formu-
lation of the model, language-neutral) denotation shared by all of its near-synonyms.
The core denotation must be specified at a level of granularity sufficient to form a use-
ful cluster of near-synonyms (i.e., at the right level of granularity so that, for instance,
human and person fall into the same cluster, but dwarf and giant do not; see Section 4).
A core denotation is represented as a directed graph of concepts linked by rela-
tions. The graph can be of arbitrary size, from a single concept (such as Generic-Error)
up to any number of interrelated concepts (as shown in Figures 7 and 8). It must
be specified in enough detail, however, for the peripheral concepts to also be speci-
fied. For instance, in the error cluster, it was not possible to use the simple concept
Generic-Error, because the peripheral concepts of the cluster refer to finer-grained
aspects of the concept (the actor and the deviation); hence we used a finer-grained
representation of the concept.
5.3 Peripheral Concepts
Peripheral concepts form the basic vocabulary of fine-grained denotational distinc-
tions. They are used to represent non-necessary and indirect aspects of word meaning.
That is, they are concepts that might be implied, suggested, emphasized, or otherwise
when a word is used, but not always. For instance, in differentiating the error words, a
lexicographer would first decide that the basic peripheral concepts required might be
‘stupidity’, ‘blameworthiness’, ‘criticism’, ‘misconception’, ‘accidentalness’, and ‘inat-
tention’. Then the lexicographer would proceed to distinguish the near-synonyms in
terms of these concepts, for instance, by specifying that blunder involves a higher
degree of blameworthiness than error.
More formally, peripheral concepts are structures of concepts defined in the same
ontology that core denotations are defined in. In fact, every peripheral concept in a
cluster must “extend” the core denotation in some way, because, after all, peripheral
concepts represent ideas related to the core meaning of a cluster of near-synonyms.
But peripheral concepts are represented separately from the core denotation.
Moreover, since peripheral concepts are defined in the ontology, they can be rea-
soned about, which, in principle, makes the formalism robust to variation in repre-
sentation. That is, if a lexicographer used, say, ‘responsibility’ to define mistake and
‘blameworthiness’ to define blunder, the words could still be compared, because in-
ference would find a connection between ‘responsibility’ and ‘blameworthiness’. See
Section 6.1 below for more discussion on this point.
5.4 Distinctions between Near-Synonyms
Following Hirst (1995), we would like to represent differences explicitly as first-class
objects (so that we can reason about them during processing). While we don’t adopt
an explicit formalism, for reasons of practicality of representation, our implicit for-
malism provides a method for computing explicit differences as needed (as we’ll
see in Section 6.1). Thus we associate with each near-synonym in a cluster a set of
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Table 2
Examples of distinctions of words.
Denotational distinctions:
Binary: blunder: (usually medium implication Stupidity)
Continuous: blunder: (always medium implication
(Blameworthiness (DEGREE high)))
Discrete: order: (always medium implication
(Authority (ATTRIBUTE (Peremptory))))
Expressive distinctions:
blunder: (always medium pejorative V1)
Stylistic distinctions:
blunder: (high concreteness)
error: (low concreteness)
Note: See the Appendix for the details. In the expressive distinction, V1
is a variable that refers to the actor of the error as specified in the core
denotation, and in the denotational distinction high is a fuzzy set of values
in the range [0, 1].
distinctions that are taken to be relative within the cluster; the cluster establishes the
local frame of reference for comparing them. So a word’s set of distinctions implic-
itly differentiates the word from its near-synonyms. In other words, if one consid-
ers the peripheral concepts, attitudes, styles, and so on, to be dimensions, then the
set of distinctions situates a word in a multidimensional space relative to its near-
synonyms. We define three types of distinction below: denotational, expressive, and
stylistic.
5.4.1 Denotational Distinctions. In our formalism, each denotational distinction refers
to a particular peripheral concept and specifies a value on that dimension, which can
be binary (i.e., is or isn’t expressed), continuous (i.e., takes a possibly fuzzy value in
the range [0, 1]), or discrete (i.e., takes a conceptual structure as a value).
Now, in Section 2.3.1 we observed that indirectness forms a continuum (sugges-
tion, implication, denotation), and, following the method used by lexicographers in near-
synonym guides, points on the continuum are modulated up or down by a strength,
which can take the values weak, medium, or strong. To also account for context depen-
dence at least as well as lexicographers do, we include a measure of the frequency
with which the peripheral concept is conveyed by the word. This can take one of five
values (never, seldom, sometimes, often, always). When the problem of context dependence
is better understood, this part of the formalism will need to be changed.
Thus, a denotational distinction of a word w is a quadruple of components as
follows:
w: (frequency strength indirectness concept)
The first part of Table 2 gives some examples for the distinctions of Figures 7 and 8.
5.4.2 Expressive Distinctions. Since a word can express a speaker’s attitude toward
potentially any entity in a situation, an expressive distinction must include a reference
to the entity. As for the attitude itself, we take a conservative approach, for now, and
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Computational Linguistics Volume 28, Number 2
define only three possible attitudes: favorable, neutral, and pejorative. Thus, an expressive
distinction has the following form:
w: (frequency strength attitude entity)
Frequency and strength have the same role as above. The entity is actually a reference
(i.e., a variable) to one of the concepts specified in the core denotation of peripheral
concepts. The second part of Table 2 gives an example.
5.4.3 Stylistic Distinctions. Although we take a rather basic approach to representing
stylistic distinctions, that does not imply that style is easy to capture. Style is one of the
most difficult of lexical phenomena to account for, since it affects the text at a pragmatic
level and is highly influenced by context. Since there is as yet no comprehensive theory
of style, our approach is similar to past approaches, such as those of DiMarco and Hirst
(1993), Stede (1993), and Hovy (1988).
Unlike the denotational distinctions discussed above, stylistic features have a
global or absolute quality to them. We can compare all words, whether or not they are
near-synonyms, on various stylistic dimensions, such as formality and concreteness.
Because style is a global aspect of text, a certain style can be (and should be) achieved
by more than just lexical choice; structural choices are just as important (DiMarco and
Hirst 1993). Hence, in defining a set of stylistic dimensions, we must look for global
stylistic features that can be carried not only by words but also by syntactic and larger
text structures. Our stylistic dimensions include, but are not limited to, formality,
force, concreteness, floridity, and familiarity.
Stylistic variation also differs from the other types of variation in being related
solely to the lexeme itself and not to its denotation or conceptual meaning (though
in a deeper sense style is certainly related to meaning). So in representing stylistic
distinctions we don’t have to make any reference to entities or other aspects of the core
denotation or peripheral concepts in a cluster. Thus, we represent a stylistic distinction
as follows:
w: (degree dimension)
where degree can take a value of low, medium,orhigh (though more values could easily
be added to increase the precision). The third part of Table 2 gives two examples.
6. Lexical Similarity
It is not sufficient merely to represent differences between near-synonyms; we must
also be able to use these representations effectively. For lexical choice, among other
tasks, we need to be able to compare the similarities of pairs of near-synonyms. For
example, in a transfer-based MT system, in order to translate the French word bavure
into English, we need to compare the similarities of at least the three pairs bavure : error,
bavure : mistake, and bavure : blunder and choose the English word whose meaning
is closest to bavure, subject to any constraints arising from the context. And in text
generation or interlingual MT, we need to be able to compare the similarities of each of
several near-synonyms to a particular semantic representation or conceptual structure
in order to choose the one that is closest to it in meaning.
Now, the general problem of measuring the semantic distance between words
or concepts has received much attention. This century, Wittgenstein (1953) formu-
lated the notion of family resemblance—that several things can be related because
125
Edmonds and Hirst Near-Synonymy and Lexical Choice
they overlap with respect to a set of properties, no property being common to all of
the words—which Rosch (1978) then used as the basis for the prototype theory of
meaning. Recent research in computational linguistics has focused more on develop-
ing methods to compute the degree of semantic similarity between any two words,
or, more precisely, between the simple or primitive concepts
15
denoted by any two
words.
There are many different similarity measures, which variously use taxonomic lex-
ical hierarchies or lexical-semantic networks, large text corpora, word definitions in
machine-readable dictionaries or other semantic formalisms, or a combination of these
(Dagan, Marcus, and Markovitch 1993; Kozima and Furugori 1993; Pereira, Tishby, and
Lee 1993; Church et al. 1994; Grefenstette 1994; Resnik 1995; McMahon and Smith 1996;
Jiang and Conrath 1997; Sch ¨utze 1998; Lin 1998; Resnik and Diab 2000; Budanitsky
1999; Budanitsky and Hirst 2001, 2002). Unfortunately, these methods are generally un-
helpful in computing the similarity of near-synonyms because the measures lack the
required precision. First, taxonomic hierarchies and semantic networks inherently treat
near-synonyms as absolute synonyms in grouping near-synonyms into single nodes
(e.g., in WordNet). In any case, as we argued in Section 3, taxonomies are inappropriate
for modeling near-synonyms. Second, as we noted in Section 2.2, standard dictionary
definitions are not usually fine-grained enough (they define the core meaning but not
all the nuances of a word) and can even be circular, defining each of several near-
synonyms in terms of the other near-synonyms. And third, although corpus-based
methods (e.g., Lin’s [1998]) do compute different similarity values for different pairs
of near-synonyms of the same cluster, Church et al. (1994) and Edmonds (1997) show
that such methods are not yet capable of uncovering the more subtle differences in
the use of near-synonyms for lexical choice.
But one benefit of the clustered model of lexical knowledge is that it naturally
lends itself to the computation of explicit differences or degrees of similarity between
near-synonyms. Although a fully effective similarity measure for near-synonyms still
eludes us, in this section we will characterize the problem and give a solution to one
part of it: computing the similarity of individual lexical distinctions.
6.1 Computing the Similarity of Near-Synonyms
In the clustered model of lexical knowledge, a difference between two near-synonyms
is encoded implicitly in two sets of relative distinctions. From two such sets of distinc-
tions, one can compute, or build, an explicit representation of the difference between
two near-synonyms. Thus, the difference between, or similarity of, two near-synonyms
depends on the semantic content of their representations on the subconceptual/stylistic
level (cf. Resnik and Diab [2000], in which similarity is computed according to the
structure, rather than content, of lexical conceptual structure representations of verbs;
see Jackendoff [1983] and Dorr [1993]).
Comparing two sets of distinctions is not straightforward, however, because, near-
synonyms often differ on seemingly incommensurate dimensions. That is, the dis-
tinctions of one near-synonym will often not match those of another near-synonym,
leaving no basis for comparison. For instance, in Figure 9, bavure and mistake align on
only two of five denotational dimensions (Blameworthiness and Criticism), and this
assumes that each of the near-synonyms was represented using the exact same pe-
15 By primitive concepts, we mean named concepts, or concepts that can be lexicalized by a single word,
even though they may be defined in terms of other concepts in an ontology.
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Computational Linguistics Volume 28, Number 2
Diff ("bavure" / "mistake") =
(( [usually / unknown] [medium / unknown] [implication / unknown]
(Stupidity (ATTRIBUTE-OF V1)) )
( [always / sometimes] medium implication
(Blameworthiness (ATTRIBUTE-OF V1) (DEGREE [more / ])) )
( always medium implication\\
(Criticism (ACTEE V1) (ATTRIBUTE (Severity (DEGREE [more / ])))) )
( [unknown / always] [unknown / medium] [unknown / implication]
(Misconception (CAUSE-OF V2) (ACTOR V1)) )
( [unknown / always] [unknown / weak] [unknown / implication]
(Accident (CAUSE-OF V2) (ACTOR V1)) )
( [always / unknown] [medium / unknown] [implication / unknown]
(Unfortunate (ATTRIBUTE-OF ROOT)) )
( [usually / always] medium [pejorative / neutral] V1 )
( [more / ] concreteness ) )
Figure 9
A structure that explicitly represents the difference between bavure and mistake. The separate
structures were merged, and where they differed, the two values are shown within square
brackets separated by a /.
ripheral concepts to begin with (i.e., both with Blameworthiness rather than, say,
one with Blameworthiness and the other with a closely related concept such as
Responsibility). Can one even compare an error that is caused by a misconception
to an error that is stupid? (See Figure 3 for bavure.)
When several dimensions are commensurate, how should one compute similarity?
Consider the near-synonyms of forest: Is it possible to decide whether a “large and
wild” tract of trees is closer to a “small wild” one or to a “medium-sized non-wild”
one? In other words, how much of a change in the size of a forest will compensate for
an opposite change in its wildness?
Part of the solution lies in the fact that the dimensions of any cluster are never
actually completely incommensurate; usually they will have interrelationships that can
be both modeled in the ontology and exploited when comparing the representations
of words. For instance, in the cluster of near-synonyms of forest, the wildness of a tract
of trees is related to its size and distance from civilization (which one can infer from
one’s knowledge about forests and wildlife; e.g., most wildlife tries to avoid people);
so there may be a way of comparing a “wild” tract of trees to a “large” tract of trees.
And in the error cluster, the dimensions are related in similar ways because of their
semantics and pragmatics (e.g., responsibility leads to blameworthiness, which often
leads to criticism, and stupidity often leads to a pejorative attitude). Certainly these
interrelationships influence both what can be coherently represented in a cluster and
how similar near-synonyms are. And such relationships can be represented in the
knowledge base, and hence reasoned about; a complete model, however, is out of the
scope of this article.
The interaction of the dimensions within a cluster is not yet very well studied, so
for a partial solution, we make the simplifying assumptions that the dimensions of a
cluster are independent and that each can be reduced to a true numeric dimension.
16
16 Certainly, numeric values are necessary at some level of representation. As we’ve seen, nuances of
meaning and style are not always clear-cut but can be vague, fuzzy, and continuously variable. Using a
numerical method would seem to be the most intuitive way of computing similarity, which we have to
do to compare and choose appropriate lexical items.
127
Edmonds and Hirst Near-Synonymy and Lexical Choice
Thus, two distinctions d
1
and d
2
are commensurate if the following two conditions
hold:
• d
1
and d
2
are of the same type (i.e., stylistic, expressive, or denotational).
• If d
1
and d
2
are stylistic, then they involve the same stylistic dimension;
if they are expressive, then they refer to the same entity; and if they are
denotational, then they involve the same peripheral concept.
6.2 Computing the Similarity of Distinctions
Given our simplifications from above, a word’s set of distinctions situates it in a numeric
multidimensional space. Consider a function Sim: D × D → [0, 1], for computing the
similarity of two commensurate lexical distinctions taken from the set D of all possible
distinctions that can be represented in a particular cluster. A value of 0 means that
the distinctions are completely different (or can’t even be compared), and a value of
1 means that they are equivalent (though not necessarily identical, as two equivalent
distinctions might be structurally different).
Hence, each type of distinction requires its own similarity function:
Sim(d
1
, d
2
)=









0.0ifd
1
and d
2
are not commensurate
Sim
denotational
(d
1
, d
2
) if d
1
and d
2
are denotational
Sim
expressive
(d
1
, d
2
) if d
1
and d
2
are expressive
Sim
stylistic
(d
1
, d
2
) if d
1
and d
2
are stylistic
(1)
Each of the similarity functions must compare the values that the pair of distinctions
has on each of their components (see Section 5.4). To arrive at a final numerical value,
we must reduce each component to a real-valued dimension and assign each symbolic
value for that component to a numeric position on the line. Edmonds (1999) gives
complete details of the formulas we developed.
There is, however, a remaining interesting problem: How does one compute the
degree of similarity of two conceptual structures? Denotational distinctions sometimes
involve complex structures of concepts, and these structures must be somehow com-
pared to determine their numeric degree of similarity. For instance, we might need
to decide how similar a high degree of blameworthiness is to a moderate degree of
blameworthiness, or to blameworthiness. Or, we might need to decide how similar
official authority is to peremptory authority, or how similar arbitrary power is to
peremptory authority (where arbitrariness is a kind of peremptoriness and authority
is a kind of power). Computing this type of similarity is clearly different from, but
related to, the problem of computing the similarity of primitive concepts (or words).
We have to consider not only the content but also the structure of the representations.
We are not aware of any research on the general problem of computing the similar-
ity of arbitrary conceptual structures, though some related work has been done in the
area of description logics. Cohen, Borgida, and Hirsh (1992), for example, formalize a
“least common subsumer” operation that returns the largest set of commonalities be-
tween two descriptions. And Resnik and Diab (2000) use a technique, attributed to Lin,
of decomposing a structure into feature sets. Edmonds (1999) describes a technique for
simultaneously traversing a pair of conceptual structures under the assumption that
the structures will be “similar” because they are commensurate. Still, a good solution
to this problem remains an open issue.
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Computational Linguistics Volume 28, Number 2
GenerationAnalysis
nuancesnuances
Recover
clusters
Interlingual
rep.
Target Text
Express
French
clusters
English
ONTOLOGY
Source Text
Context
instantiates
Figure 10
Lexical analysis and choice in machine translation.
7. Lexical Choice
7.1 Architectures for Lexical Choice
The clustered model of lexical knowledge is applicable to both the lexical-analysis
and lexical-choice phases of a machine translation system. Figure 10 shows that dur-
ing analysis, fine-grained lexical knowledge of the source language is accessed, in
conjunction with the context, to determine possibilities of what is expressed in the
source language text. Then, depending on the type of MT system (i.e., transfer or
interlingual), the appropriate target language words can be chosen: The possibilities
become preferences for choice. Recovering nuances of expression from source text is
currently an open problem, which we do not explore further here (but see Edmonds
[1998] for some preliminary work). In this section we concentrate on the second phase
of MT and show that robust, efficient, flexible, and accurate fine-grained lexical choice
is a natural consequence of a clustered model.
Lexical choice, as we see it, is more than a problem of mapping from concepts to
words, as the previous section might have implied; it is a problem of selecting words
so as to meet or satisfy a large set of possibly conflicting preferences to express certain
nuances in certain ways, to establish the desired style, and to respect collocational
and syntactic constraints. So lexical choice—genuine lexical choice—is making choices
between options rather than merely finding the words for concepts, as was the case in
many early text generation systems (for instance, BABEL [Goldman 1975], MUMBLE
[McDonald 1983], and TEXT [McKeown 1985]). This kind of lexical choice is now
thought to be the central task in text generation (or, at least, sentence generation),
because it interacts with almost every other task involved. Indeed, many recent text
generation systems, including MOOSE (Stede 1999), ADVISOR II (Elhadad, McKeown,
and Robin 1997), and Hunter-Gatherer (Beale et al. 1998), among others (see Reiter
and Dale’s [1997] survey), adopt this view, yet their lexical-choice components do not
account for near-synonymy. Without loss of generality, we will look at fine-grained
lexical choice in the context of one of these systems: Stede’s MOOSE (1999).
The input to MOOSE is a “SitSpec,” that is, a specification of a situation repre-
sented on the conceptual–semantic level as a graph of instances of concepts linked
129
Edmonds and Hirst Near-Synonymy and Lexical Choice
by relations. MOOSE outputs a complete well-formed “SemSpec,” or semantic speci-
fication on the syntactic–semantic level, from which the Penman sentence realization
system can generate language.
17
MOOSE processes the input in two stages. It first
gathers all of the lexical items (as options for choice) whose conceptual–semantic rep-
resentation covers any part of the SitSpec. Then it chooses a set of lexical items that
satisfy Stede’s three criteria for sentence planning: the input SitSpec is completely cov-
ered (and so is completely lexicalized without redundancy); a well-formed SemSpec
can be built out of the partial SemSpecs associated with each of the chosen lexical
items; and as many of the preferences are satisfied as possible. MOOSE supports pref-
erences, but only those that require structural decisions, such as choosing a causative
over inchoative verb alternation. The main goal of Stede’s work was to account for
structural paraphrase in sentence generation, not near-synonymy.
In the general case of sentence planning, given a set of input constraints and
preferences, a sentence planner will make a large number of decisions of different
types—lexical, syntactic, and structural—each of which has the potential to satisfy any,
some, or all of the input preferences (while trying to satisfy the constraints, of course).
It is unlikely that any particular set of preferences can all be satisfied simultaneously,
so some kind of conflict resolution strategy is required in order to manage the decision-
making task. It is not within the scope of this paper to develop solutions to this general
problem (but see Nirenburg, Lesser, and Nyberg [1989], Wanner and Hovy [1996],
Elhadad, McKeown, and Robin [1997], and Stede [1999] for a variety of solutions).
Instead, we will discuss the following two new issues that arise in managing the
interactions between lexical choices that a clustered model brings out:
• We will argue for a unified model for representing any type of
preference for lexical choice.
• We describe a two-tiered model of lexical choice that is the
consequence of a clustered model of lexical knowledge.
Then, we will end the section with a brief description of our software implementation
of the model, called I-Saurus.
7.2 Constraints and Preferences
Simple systems for lexical choice need only to make sure that the denotations of the
words chosen in response to a particular input exactly match the input. But when
we use fine-grained aspects of meaning, the lexical-choice process, and so, in turn, its
input, will ultimately be more complex. But with so many possibilities and options,
choosing from among them will necessarily involve not only degrees of satisfying
various criteria, but also trade-offs among different criteria. Some of the criteria will be
hard constraints (i.e., a SitSpec), ensuring that the basic desired meaning is accurately
conveyed, and others will be preferences.
The main difference between a constraint and a preference is that a preference
is allowed to be satisfied to different degrees, or even not at all, depending on the
decisions that are made during sentence planning. A preference can be satisfied by
17 A SemSpec is a fully lexicalized sentence plan in Penman’s Sentence Plan Language (SPL). SPL is
defined in terms of the Penman Upper Model, a model of meaning at the syntactic–semantic level,
which ensures that the SemSpec is well-formed linguistically. Penman can thus turn any SemSpec into
a well-formed sentence without having to make any open-class lexical decisions (Penman Natural
Language Group 1989; Stede 1999)
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Computational Linguistics Volume 28, Number 2
a single decision or collectively by a group of decisions.
18
And because conflicts and
trade-offs might arise in the satisfaction of several preferences at once, each preference
must have an externally assigned importance factor.
Many types of preference pertain to lexical choice, including emphasizing an as-
pect of an entity in a situation, using normal words or a certain dialect, using words
with a particular phonology (e.g., words that rhyme), using different near-synonyms
for variety or the same word as before for consistency, and so on. All should be
formalizable in a unified model of preference, but we have started with three types
corresponding to the subconceptual level of the clustered model: denotational (or se-
mantic), expressive, and stylistic preferences.
Denotational preferences are distinct from denotational constraints, but there is
no theoretical difference in the nature of a “preferred” meaning to a “constrained”
meaning. Hence, we can represent both in the same SitSpec formalism. Thus, a deno-
tational preference is a tuple consisting of a partial SitSpec and a preferred method of
expression, which takes a value on the continuum of indirectness (see Section 5.4). An
expressive preference requests the expression of a certain attitude toward a certain
entity that is part of the situation. Thus, an expressive preference is a tuple consist-
ing of a reference to the entity and the stance that the system should take: favor,
remain neutral, or disfavor. A stylistic preference, for now, is simply a value (of low,
medium, or high) on one of the stylistic dimensions. We will see some examples in
Section 8.
7.2.1 Satisfying Preferences by Lexical Choice. In the best case, it will be possible
to simultaneously satisfy all of the input preferences by choosing appropriate near-
synonyms from appropriate clusters. But if none of the available options will sat-
isfy, to any degree, a particular preference, then that preference is trivially impossible
to satisfy (by lexical choice). But even when there are options available that satisfy
a particular preference, various types of conflicts can arise in trying to satisfy sev-
eral preferences at once, making it impossible to use any of those options. At the
level of clusters, for instance, in choosing a particular cluster in order to satisfy one
preference, we might be therefore unable to satisfy another preference that can be
satisfied only by a different, competing cluster: We might choose the cluster of the
err verbs (to err, to blunder) because of the simplicity or directness of its syntax: John
erred; but we would not be able simultaneously to satisfy a preference for implying
a misconception by choosing, say, mistake from the cluster of error nouns: John made a
mistake.
Similar trade-offs occur when choosing among the near-synonyms of the same
cluster. Such lexical gaps, where no single word can satisfy all of the input preferences
that a cluster can potentially satisfy, are common. For instance, in English, it’s hard
to talk about a mistake without at least some overtones of criticism; in Japanese one
can: with ayamari instead of machigai (Fujiwara, Isogai, and Muroyama 1985). There
is also no near-synonym of error in English that satisfies preferences to imply both
stupidity and misconception; blunder satisfies the former but not the latter, and mistake
vice versa. Similarly, there is no formal word for an untrue statement (i.e., a lie) that
also expresses that the lie is insignificant; fib is an option, but it is not a formal word.
And there is no word for a tract of trees that is both large and not wild; forest has the
former property, woods the latter.
18 A preference is like a floating constraint (Elhadad, McKeown, and Robin 1997) in that it can be
satisfied by different types of decision in sentence planning but differs in that it may be satisfied to
different degrees.
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Edmonds and Hirst Near-Synonymy and Lexical Choice
Two separate simultaneous choices might also conflict in their satisfaction of a sin-
gle preference. That is, the preference might be satisfied by one choice and negatively
satisfied by another choice. For instance, it might happen that one word is chosen in
order to express a favorable attitude toward a participant in a particular situation and
another word is chosen that inadvertently expresses a pejorative attitude toward the
same person if that second word is chosen in order to satisfy some other preference.
And of course, stylistic decisions can often conflict (e.g., if one has to choose both
formal and informal words).
Our solution to resolving such lexical gaps and conflicting preferences is to use an
approximate matching algorithm that attempts to satisfy collectively as many of the
preferences as possible (each to the highest degree possible) by choosing, on two tiers,
the right words from the right clusters.
19
We will describe this model in Section 7.3.
7.2.2 Compatibility of Preferences. But what happens when it is impossible to simul-
taneously satisfy two preferences under any circumstances? We have assumed up to
now that the set of preferences in the input is consistent or well-formed. This is often a
reasonable assumption. In the context of MT, for instance, we can assume that a “good”
analysis stage would output only well-formed expressions free of incompatibilities.
But two preferences may be incompatible, and we would like our system to be
able to detect such situations. For instance, preferences for both low and high severity
are incompatible; not only is it impossible for a word to simultaneously express both
ideas, but if the system were to attempt to satisfy both, it might output a dissonant
expression
20
such as “I (gently) chided Bill for his (careless) blunder” (the preference
to harshly criticize Bill is satisfied by blunder, and the preference to gently criticize Bill
is satisfied by chide). (Of course, a dissonant expression is not always undesirable; it
might be used for special effect.) This kind of incompatibility is easy to detect in our
formalism, because peripheral concepts are explicitly modeled as dimensions. There
are, of course, other types of incompatibility, such as denotational and contextual
incompatibilities, but we won’t discuss them further here (see Edmonds [1999]).
7.3 Two-Tiered Lexical Choice
Assume now that all of the options for choice have been identified by the system. In
our system, these options are the clusters whose core denotations match part of the
input SitSpec. Ignoring the coverage and well-formedness constraints for now, two
different, mutually constraining types of decision must be made:
• Choosing from several competing cluster options.
• Choosing a near-synonym from a cluster option.
We believe that it is important to separate the processes for making these two types
of decision—even though they must interact—because of their different choice criteria
and effects. The former type involves choosing between options of differing coarse-
grained semantic content and resulting syntactic structure (i.e., paraphrases): clusters
19 A complementary approach is to paraphrase the input and hence explicitly express a preferred
implication or mitigate against an unwanted implication (for instance, by generating insignificant lie
when fib is too informal). A sentence planner, like MOOSE, is designed to generate such structural
paraphrases, so we have concentrated on the lexical issues here.
20 Dissonance is one form of semantic anomaly that Cruse (1986) defines by example: “Arthur is a
married bachelor.”
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Computational Linguistics Volume 28, Number 2
have different core denotations, after all. Here, issues of syntactic and semantic style
are involved, as one can choose how the semantic content is to be incorporated. On
the other hand, the latter type of decision involves options that might have subtle
semantic and stylistic differences but result in the same syntactic structure (though
collocational and subcategorization structure can vary).
In other words, lexical choice is a two-tiered process that must find both the
appropriate set of cluster options and the appropriate set of lexical items (one from
each chosen cluster option) whose contributing SemSpec fragments can be unified into
a complete well-formed SemSpec. Of course, many possible SemSpecs can usually be
generated, but the real problem is to find the combination of cluster options and lexical
items that globally satisfy as many of the input preferences as possible.
For instance, Figure 11 depicts the state of processing the SitSpec for the utterance
by John of an untrue statement just before lexical choice occurs. There are four clus-
ter options (denoted by the suffix C): say C and tell-a-lie C match subgraphs of
the SitSpec rooted at say1, untruth C matches the graph rooted at lie1, and John C
matches john1. Now, the system could choose the tell-a-lie C cluster and the John C
cluster, which fully cover the SitSpec, and then choose the words John and lie to come
up with John lies, or the system could choose John and prevaricate for John prevaricates.
The system could also choose the say C, untruth C and John C clusters, and then the
words tell, fib, and John, to end up with John tells a fib. These alternatives—there are
many others—are different in structure, meaning, and style. Which best satisfies the
input preferences, whatever they may be?
We can formally define fine-grained lexical choice (within sentence planning) as
follows. Given an input SitSpec S and a set of compatible preferences P, the goal is to
find a set C of i cluster options and a word w
i
from each c
i
∈ C such that
• every node of S is covered by exactly one c
i
• the partial SemSpecs of all the words w
i
can be combined into a
well-formed SemSpec SP
• Satisfaction(P, SP) is maximized over all possible SemSpecs
The first criterion ensures complete coverage without redundancy of the input SitSpec,
so the desired meaning, at a coarse grain, is expressed; the second ensures that a
SemSpec can be constructed that will lead to a grammatical sentence; and the third
ensures that the preferences are collectively satisfied as much as is possible by any
sentence plan. The third criterion concerns us here; the first two are dealt with in
MOOSE.
As we said earlier, a thorough understanding of the interacting decisions in lexical
choice is still an open problem, because it is context dependent. Our present solution
is simply to assume that no complex interaction between decisions takes place. So,
assuming that each option has an associated numeric score (the degree to which it
satisfies all of the preferences), we can simply choose the set of options that maximizes
the sum of the scores, subject to the other constraints of building a proper sentence
plan. Thus, we do not provide a solution to how the context affects the combination
of the scores. So, given a sentence plan SP and a set of preferences P, we have
Satisfaction(P, SP)=
summationdisplay
w∈SP
WSat(P, w). (2)
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Edmonds and Hirst Near-Synonymy and Lexical Choice
say_C
Cluster option
untruth_C
John_C
SitSpec
tell-a-lie_C
Nonconformity
equivocatelie
Thing
Untruth
fib
Person
Communicate
Person
Statement
lie
prevarication
John
falsehood
misrepresentation
John
fib
nonconform1
john1 lie1
say1
untruth
Communicate
prevaricate
Person
say
tell
ATTRIBUTESAYER
ATTRIBUTE
SAYER
SAYING
SAYING
SAYER
SAYER
SAYINGSAYER
Figure 11
The state of processing just before lexical choice on the input for John tells a lie. Four clusters
have become options; each is shown with its core denotation and near-synonyms. Solid arrows
in the SitSpec indicate relations between instances of concepts. Solid arrows in the cluster
options relate concepts in the core denotations. Dashed arrows link SitSpec nodes to the
cluster options that cover subgraphs rooted at the nodes.
where WSat is the degree to which w satisfies the preferences P (see Equation (3)).
The most preferred sentence plan SP
prime
is thus the one whose set of word choices
maximizes Satisfaction(P, SP
prime
). This function accounts for trade-offs in the satisfaction
of preferences, because it finds the set of words that collectively satisfy as many of the
preferences as possible, each to the highest degree possible.
Each word in SP has to be chosen from a distinct cluster. Thus, given
• a particular cluster c in the set of all cluster options
• a list W of candidate near-synonyms of c, ordered according to a
prespecified criterion (some candidates of the cluster might have already
been ruled out because of collocational constraints)
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Computational Linguistics Volume 28, Number 2
• a set P
c
⊂ P of compatible preferences, each of which can potentially be
satisfied by a word in c, with associated importances: Imp: P → [0, 1]
find the first candidate w
prime
∈ W such that WSat(P, w
prime
) is maximized.
We use an approximate-matching algorithm to compute WSat(P, w). Under the
simplification that its value depends on the degree to which w individually satisfies
each of the preferences in P
c
, the algorithm computes WSat(P, w) by combining the
set of scores Sat(p, w) for all p ∈ P
c
. Various combination functions are plausible, in-
cluding simple functions, such as a weighted average or a distance metric, and more
complex functions that could, for instance, take into account dependencies between
preferences.
21
Deciding on this function is a subject for future research that will em-
pirically evaluate the efficacy of various possibilities. For now, we define WSat as a
weighted average of the individual scores, taking into account the importance factors:
WSat(P, w)=WSat(P
c
, w)=
summationdisplay
p∈P
c
Imp(p)
|P
c
|
Sat(p, w)(3)
For a given preference p ∈ P
c
, the degree to which p is satisfied by w, Sat(p, w),
is reducible to the problem of computing similarity between lexical distinctions, for
which we already have a solution (see Equation (1)). Thus,
Sat(p, w)=Sim(d(p), d(w)) (4)
where d(p) is a kind of pseudo-distinction generated from p to have the same form
as a lexical distinction, putting it on equal footing to d(w), and d(w) is the distinction
of w that is commensurate with d(p), if one exists.
7.4 Implementation: I-Saurus
I-Saurus, a sentence planner that splits hairs, extends Stede’s MOOSE (1999) with the
modifications formalized above for fine-grained lexical choice. It takes a SitSpec and
a set of preferences as input, and outputs a sentence plan in Penman’s SPL, which
Penman generates as a sentence in English. (Section 8 provides an example.)
Now, finding the best set of options could involve a lengthy search process. An
exhaustive search through all possible sentence plans to find the one that maximizes
Satisfaction(P, SP) can be very time-inefficient: In the relatively small example given in
Section 8, there are 960 different sentence plans to go through. To avoid an exhaustive
search, we use the following heuristic, adopted from Stede (1999): In order to find
the globally preferred sentence plan, make the most preferred local choices. That is,
whenever a (local) decision is made between several options, choose the option with
the highest score. Thus, we postulate that the most preferred sentence plan will be
one of the first few sentence plans generated, though we offer no proof beyond our
intuition that complex global effects are relatively rare, which is also a justification for
the simplifications we made above.
Figure 12 gives an algorithm for two-tiered lexical choice embedded in MOOSE’s
sentence planner. The main additions are the procedures Next-Best-Cluster-Option and
21 For instance, we might want to consider a particular preference only after some other preference has
been satisfied (or not) or only to resolve conflicts when several words satisfy another preference to the
same degree.
135
Edmonds and Hirst Near-Synonymy and Lexical Choice
Build-Sentence-Plan(node, P)
(1) c ←Next-Best-Cluster-Option(node, P)
if we’ve tried all the options then return “fail”
(2) w ←Next-Best-Near-Synonym(c, P)
if we’ve tried all the near-synonyms in c then backtrack to (1)
p ← partial SemSpec of w
if p has external variables then
for each external variable v in p
s ← Build-Sentence-Plan(node bound to v, P)
if s = “fail” then
backtrack to (2)
else
attach s to p at v
return p
Figure 12
The sentence-planning algorithm. This algorithm outputs the most preferred complete
well-formed SemSpec for a subgraph rooted at given node in the SitSpec.
Next-Best-Near-Synonym. (Note, however, that this version of the algorithm does not
show how complete coverage or well-formedness is ensured.) Next-Best-Cluster-Option
moves through the cluster options that cover part of the SitSpec rooted at node in
order of preference. As we said above, structural decisions on this tier of lexical choice
are outside the scope of this article, but we can assume that an algorithm will in due
course be devised for ranking the cluster options according to criteria supplied in the
input. (In fact, MOOSE can rank options according to preferences to foreground or
background participants, in order to make them more or less salient, but this is only
a start.) Next-Best-Near-Synonym steps through the near-synonyms for each cluster in
order of preference as computed by WSat(P, w).
7.5 Summary
The two-tiered lexical-choice algorithm (and sentence-planning algorithm) developed
in this section is as efficient as any algorithm developed to date for a conventional
model of lexical knowledge (without near-synonyms), because it can find the appro-
priate cluster or clusters just as easily as the latter can find a word; A cluster in our
model corresponds to an individual word in the conventional model. And choosing a
near-synonym from a cluster is efficient because there are normally only a few of them
per cluster. The system does not have to search the entire lexicon. The full complex-
ity of representing and using fine-grained lexical differences is partitioned into small
clusters. The process is also robust, ensuring that the right meaning (at a coarse grain)
is lexicalized even if a “poor” near-synonym is chosen in the end. And when the right
preferences are specified in the input, the algorithm is accurate in its choice, attempting
to meet as many preferences as possible while also satisfying the constraints.
8. Example
A formal evaluation of I-Saurus would require both a substantial lexicon of clusters
and a large test suite of input data correlated with the desired output sentences. Build-
ing such a suite would be a substantial undertaking in itself. Barring this, we could
136
Computational Linguistics Volume 28, Number 2
Table 3
Four simultaneous preferences and the six candidates of the untruth C cluster.
Preferences:
1 (imply (significance1 (ATTRIBUTE-OF lie1) (DEGREE low)))
2 (imply (intend1 (ACTOR john1) (ACTEE mislead1)))
3 (disfavor john1)
4 (low formality)
Candidate
Preference fib lie misrepresentation untruth prevarication falsehood
1 Insignificance 1.00 0.00 0.00 0.00 0.00 0.00
2 Deliberateness 0.50 1.00 0.75 0.25 0.75 0.00
3 Disfavor 0.50 0.63 0.50 0.50 0.50 0.50
4 Low formality 1.00 0.50 0.50 0.50 0.00 0.50
Total Score 3.00 2.13 1.75 1.25 1.25 1.00
Note: For each candidate, we show the satisfaction scores (Sat) for each individual
preference and the total satisfaction scores (WSat): fib scores highest.
evaluate I-Saurus as an MT system, in terms of coverage and of quality (intelligibility,
fidelity, and fluency). Unfortunately, I-Saurus is but a prototype with a small exper-
imental lexicon, so we can only show by a few examples that it chooses the most
appropriate words given a variety of input preferences.
Returning again the situation of John and his lie (Figure 11), consider the set of four
simultaneous preferences shown in the top part of Table 3. The bottom part shows the
scores for each candidate in the untruth C cluster. If this cluster option were chosen,
then I-Saurus would choose the noun fib, because it simultaneously and maximally
satisfies all of the preferences as shown by the score of WSat({1, 2, 3, 4}, fib)=3.00. But
note that if fib were not available, then the second-place lie would be chosen, leaving
unsatisfied the preference to express insignificance.
Now, for a whole sentence, consider the SitSpec shown in Table 4. For this, I-
Saurus can generate 960 different sentence plans, including plans that realize the
sentences John commands an alcoholic to lie and John orders a drunkard to tell a fib.I-
Saurus can be so prolific because of the many possible combinations of the near-
synonyms of the six clusters involved: John C (one near-synonym), alcoholic C (ten
near-synonyms), order C (six near-synonyms), say C (two near-synonyms), untruth C
(six near-synonyms), and tell-a-lie C (four near-synonyms).
The bottom part of Table 4 shows the variety of output that is possible when each
individual preference and various combinations of simultaneous preferences (cases
i–x) are input to the system. (The numbered preferences are listed at the top of the
table.) So for example, if we input preference 3 (high formality), the system outputs
John enjoins an inebriate to prevaricate. The output appears stilted in some cases because
no other parameters, such as desired verb tense, were given to Penman and because
the system has no knowledge of collocational constraints. Of course, we could have
defined many other preferences (and combinations of preferences), but we chose these
particular ones in order to show some of the interesting interactions that occur among
the cluster options during processing; they are not meant to be representative of what
a user would normally ask of the system.
Consider, for instance, case iv. Here, one cluster can satisfy preference 6 (pejorative
attitude), and another cluster can satisfy preference 10 (misconception), but neither
137
Edmonds and Hirst Near-Synonymy and Lexical Choice
Table 4
A sample of output sentences of I-Saurus given an input SitSpec and various preferences and
combinations of preferences (cases i–x).
SitSpec:
(order1 (SAYER john1)
(SAYEE alcoholic1)
(SAYING (perform1 (ACTOR alcoholic1)
(ACTEE (tell1 (SAYER alcoholic1)
(SAYING (lie1
(ATTRIBUTE nonconform1))))))))
Preferences:
1 (low formality)
2 (medium formality)
3 (high formality)
4 (high concreteness)
5 (favor alcoholic1)
6 (disfavor alcoholic1)
7 (imply (authority1 (ATTRIBUTE-OF john1) (ATTRIBUTE official1)))
8 (imply (authority1 (ATTRIBUTE-OF john1) (ATTRIBUTE peremptory1)))
9 (imply (significance1 (ATTRIBUTE-OF lie1) (DEGREE low)))
10 (imply (misconceive1 (ACTOR alcoholic1) (CAUSE-OF lie1)))
11 (imply (contradict2 (ACTOR lie1) (ATTRIBUTE categorical2)))
Case Input preferences Output
None John commands an alcoholic to lie.
1 John commands a drunk to fib.
2 John commands an alcoholic to lie.
3 John enjoins an inebriate to prevaricate.
4 John directs a drunkard to tell a lie.
5 John commands a tippler to fib.
6 John commands a drunk to lie.
7 John commands an alcoholic to lie.
8 John orders an alcoholic to lie.
9 John commands an alcoholic to fib.
10 John commands an alcoholic to tell an untruth.
11 John commands an alcoholic to lie.
i 2, 4 John directs a drunkard to tell a lie.
ii 1, 9 John commands a drunk to fib.
iii 3, 6 John enjoins a drunkard to prevaricate.
iv 6, 10 John commands a drunkard to tell an untruth.
v 3, 9 John enjoins an inebriate to fib.
vi 3, 7, 9 John commands an inebriate to fib.
vii 3, 8, 9 John orders an inebriate to fib.
viii 3, 6, 8, 9 John orders a drunkard to fib.
ix 3, 5 John enjoins a tippler to tell a prevarication.
x 3, 5, 11 John enjoins a tippler to tell a prevarication.
cluster can satisfy both preferences on its own. So the system chooses drunkard, because
it is pejorative, and untruth, because it implies a misconception. No other combination
of choices from the two clusters could have simultaneously satisfied both preferences.
And finally, consider case v, which illustrates a clash in the satisfaction of one of
the preferences. Fib is chosen despite the fact that it is informal, because it is the only
word that implies an insignificant lie. But the system compensates by choosing two
138
Computational Linguistics Volume 28, Number 2
other formal words: enjoin and inebriate. If we add a preference to this case to imply
that John has official authority (case vi), then I-Saurus system chooses command instead
of enjoin, further sacrificing high formality.
9. Related Work
Most computational work on near-synonymy has been motivated by lexical mismatches
in machine translation (Kameyama et al. 1991). In interlingual MT, an intermediate
representational scheme, such as an ontology in knowledge-based machine translation
(KBMT) (Nirenburg et al. 1992), or lexical-conceptual structures in UNITRAN (Dorr
1993) is used in encoding lexical meaning (and all other meaning). But as we showed
in Section 3, such methods don’t work at the fine grain necessary for near-synonymy,
despite their effectiveness at a coarse grain. To overcome these problems but retain the
interlingual framework, Barnett, Mani, and Rich (1994) describe a method of generat-
ing natural-sounding text that is maximally close in meaning to the input interlingual
representation. Like us, they define the notion of semantic closeness, but whereas they
rely purely on denotational representations and (approximate) logical inference in ad-
dition to lexical features for relative naturalness, we explicitly represent fine-grained
aspects on a subconceptual level and use constraints and preferences, which gives flex-
ibility and robustness to the lexical-choice process. Viegas (1998), on the other hand,
describes a preliminary solution that accounts for semantic vagueness and underspec-
ification in a generative framework. Although her model is intended to account for
near-synonymy, she does not explicitly discuss it.
Transfer-based MT systems use a bilingual lexicon to map words and expressions
from one language to another. Lists, sometimes huge, of handcrafted language-pair-
specific rules encode the knowledge to use the mapping (e.g., in SYSTRAN [Gerber
and Yang 1997]). EuroWordNet (Vossen 1998) could be used in such a system. Its
Inter-Lingual-Index provides a language-independent link between synsets in different
languages and has an explicit relation, EQ NEAR SYNONYM, for relating synsets that
are not directly equivalent across languages. But, as in individual WordNets, there is
no provision for representing differences between near-synonyms.
In statistical MT, there would seem to be some promise for handling near-synon-
ymy. In principle, a system could choose the near-synonym that is most probable given
the source sentence and the target-language model. Near-synonymy seems to have
been of little concern, however, in statistical MT research: The seminal researchers,
Brown et al. (1990), viewed such variations as a matter of taste; in evaluating their
system, two different translations of the same source that convey roughly the same
meaning (perhaps with different words) are considered satisfactory translations. More
recently, though, Foster, Isabelle, and Plamondon (1997) show how such a model can
be used in interactive MT, and Langkilde and Knight (1998) in text generation. Such
methods are unfortunately limited in practice, because it is too computationally ex-
pensive to go beyond a trigram model (only two words of context). Even if a statistical
approach could account for near-synonymy, Edmonds (1997) showed that its strength
is not in choosing the right word, but rather in determining which near-synonym is
most typical or natural in a given context. So such an approach would not be so useful
in goal-directed applications such as text generation, or even in sophisticated MT.
10. Conclusion
Every natural language processing system needs some sort of lexicon, and for many
systems, the lexicon is the most important component. Yet, real natural language pro-
139
Edmonds and Hirst Near-Synonymy and Lexical Choice
cessing systems today rely on a relatively shallow coverage of lexical phenomena,
which unavoidably restricts their capabilities and thus the quality of their output. (Of
course, shallow lexical semantics is a necessary starting point for a practical system,
because it allows for broad coverage.) The research reported here pushes the lexical
coverage of natural language systems to a deeper level.
The key to the clustered model of lexical knowledge is its subconceptual/stylistic
level of semantic representation. By introducing this level between the traditional con-
ceptual and syntactic levels, we have developed a new model of lexical knowledge
that keeps the advantages of the conventional model—efficient paraphrasing, lexical
choice (at a coarse grain), and mechanisms for reasoning—but overcomes its short-
comings concerning near-synonymy. The subconceptual/stylistic level is more expres-
sive than the top level, yet it allows for tractable and efficient processing because
it “partitions,” or isolates, the expressiveness (i.e., the non-truth-conditional seman-
tics and fuzzy representations) in small clusters. The model reconciles fine-grained
lexical knowledge with coarse-grained ontologies using the notion of granularity of
representation.
The next stage in this work is to build a more extensive lexicon of near-synonym
clusters than the few handwritten clusters that were built for the simple implementa-
tion described in this article. To this end, Inkpen and Hirst (2001a, 2001b) are develop-
ing a method to automatically build a clustered lexicon of 6,000 near-synonyms (1,000
clusters) from the machine-readable text of Hayakawa’s Choose the Right Word (1994).
Besides MT and NLG, we envision other applications of the model presented
in this article. For instance, an interactive dictionary—an intelligent thesaurus—would
actively help a person to find and choose the right word in any context. Rather than
merely list possibilities, it would rank them according to the context and to parameters
supplied by the user and would also explain potential effects of any choice, which
would be especially useful in computer-assisted second-language instruction. Or the
model could be applied in the automatic (post)editing of text in order to make the
text conform to a certain stylistic standard or to make a text more readable or natural
to a given audience.
We leave a number of open problems for another day, including recovering nu-
ances from text (see Edmonds [1998] for a preliminary discussion); evaluating the
effectiveness of the similarity measures; determining the similarity of conceptual struc-
tures; understanding the complex interaction of lexical and structural decisions during
lexical choice; exploring the requirements for logical inference in the model; model-
ing other aspects of fine-grained meaning, such as emphasis; and understanding the
context-dependent nature of lexical differences and lexical knowledge.
Appendix: An Example Representation: The Error Cluster
The following is the representation of the cluster of error nouns in our formalism.
Tokens ending in l represent lexical items. In upper case are either variables (for
cross-reference) or relations; it should be clear from the context which is which. Cap-
italized tokens are concepts. In lower case are values of various features (such as
“indirectness” and “strength”) defined in the model. We have not discussed many of
the implementation details in this article, including p-link and covers (see Edmonds
[1999]).
(defcluster error C
;;; from Gove (1984)
:syns (error l mistake l blunder l slip l lapse l howler l)
140
Computational Linguistics Volume 28, Number 2
:core (ROOT Generic-Error)
:p-link ((V1 (:and (Person V1) (ACTOR ROOT V1)))
(V2 (:and (Deviation V2) (ATTRIBUTE ROOT V2))))
:covers (ROOT)
:periph((P1 Stupidity (ATTRIBUTE-OF V1))
(P2 Blameworthiness (ATTRIBUTE-OF V1))
(P3 Criticism (ACTEE V1) (ATTRIBUTE (P31 Severity)))
(P4 Misconception (CAUSE-OF V2) (ACTOR V1))
(P5 Accident (CAUSE-OF V2) (ACTOR V1))
(P6 Inattention (CAUSE-OF V2) (ACTOR V1)))
:distinctions (
;; Blunder commonly implies stupidity.
(blunder l usually medium implication P1)
;; Mistake does not always imply blameworthiness, blunder sometimes.
(mistake l sometimes medium implication (P2 (DEGREE ’medium)))
(error l always medium implication (P2 (DEGREE ’medium)))
(blunder l sometimes medium implication (P2 (DEGREE ’high)))
;; Mistake implies less severe criticism than error.
;; Blunder is harsher than mistake or error.
(mistake l always medium implication (P31 (DEGREE ’low)))
(error l always medium implication (P31 (DEGREE ’medium)))
(blunder l always medium implication (P31 (DEGREE ’high)))
;; Mistake implies misconception.
(mistake l always medium implication P4)
;; Slip carries a stronger implication of accident than mistake.
;; Lapse implies inattention more than accident.
(slip l always medium implication P5)
(mistake l always weak implication P5)
(lapse l always weak implication P5)
(lapse l always medium implication P6)
;; Blunder expresses a pejorative attitude towards the person.
(blunder l always medium pejorative V1)
;; Blunder is a concrete word, error and mistake are abstract.
(blunder l high concreteness)
(error l low concreteness)
(mistake l low concreteness)
;; Howler is an informal term
(howler l low formality))
)
Acknowledgments
Our work is financially supported by the
Natural Sciences and Engineering Research
Council of Canada, the Ontario Graduate
Scholarship program, and the University of
Toronto. For discussions, suggestions, and
comments on this work, we are grateful to
Jack Chambers, Mark Chignell, Robert Dale,
Chrysanne DiMarco, Paul Deane, Steve
Green, Eduard Hovy, Brian Merrilees, John
Mylopoulos, Kazuko Nakajima, Sergei
Nirenburg, Geoffrey Nunberg, Henry
Schoght, Manfred Stede and the anonymous
reviewers of Computational Linguistics.

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