Lexical Choice Criteria in Language Generation 
Manfred Stede 
Department of Computer Science 
University of Toronto 
Toronto M5S 1A4, Canada 
mstede~cs.toronto.edu 
1 Introduction 
In natural language generation (NLG), a semantic 
representation of some kind- possibly enriched with 
pragmatic attributes -- is successively transformed 
into one or more linguistic utterances. No matter 
what particular architecture is chosen to organize 
this process, one of the crucial decisions to be made 
is lexicalization: selecting words that adequately ex- 
press the content that is to be communicated and, 
if represented, the intentions and attitudes of the 
speaker. Nirenburg and Nirenburg \[1988\] give this 
example to illustrate the lexical choice problem: If 
we want to express the meaning "a person whose 
sex is male and whose age is between 13 and 15 
years", then candidate realizations include: boy, kid, 
teenager, youth, child, young man, schoolboy, ado- 
lescent, man. The criteria influencing such choices 
remain largely in the dark, however. 
As it happens, the problem of lexical choice has 
not been a particularly popular one in NLG. For 
instance, Marcus \[1987\] complained that most gen- 
erators don't really choose words at all; McDonald 
\[1991\], amongst others, lamented that lexical choice 
has attracted only very little attention in the research 
community. Implemented generators tend to provide 
a one-to-one mapping from semantic units to lexical 
items, and their producers occasionally acknowledge 
this as a shortcoming (e.g., \[Novak, 1991, p. 666\]); 
thereby the task of lexical choice becomes a non- 
issue. For many applications, this is indeed a feasible 
scheme, because the sub-language under considera- 
tion can be sufficiently restricted such that a direct 
mapping from content to words does not present a 
drawback -- the generator is implicitly tailored to- 
wards the type of situation (or register) in which it 
operates. But in general, with an eye on more ex- 
pressive and versatile generators, this state of affairs 
calls for improvement. 
Why is lexical choice difficult? Unlike many other 
decisions in generation (e.g., whether to express an 
attribute of an object as a relative clause or an ad- 
jective) the choice of a word very often carries impli- 
catures that can change the overall message signifi- 
cantly -- if in some sentence the word boy is replaced 
with one of the alternatives above, the meaning shifts 
considerably. Also, often there are quite a few sim- 
ilar lexical options available to a speaker, whereas 
the number of possible syntactic sentence construc- 
tions is more limited. To solve the choice problem, 
first of all the differences between similar words have 
to be represented in the lexicon, and the criteria for 
choosing among them have to be established. In the 
following, I give a tentative list of choice criteria, 
classify them into constraints and preferences, and 
outline a (partly implemented) model of lexicaliza- 
tion that can be incorporated into language genera- 
tors. 
2 Word Choice Criteria 
Only few contributions have been made towards 
establishing word choice criteria in NLG. 1 Hovy's 
\[1988\] generator PAULINE selected lexical items ac- 
cording to pragmatic aspects of the situation (rhetor- 
ical goals of the speaker giving rise to stylistic goals, 
which in turn lead to certain lexical choices). Also 
looking at the pragmatic level, Elhadad \[1991\] ex- 
amined the influence of a speaker's argumentative 
intent on the choice of adjectives. Wanner and Bate- 
man \[1990\] viewed lexical choice from a situation- 
dependent perspective: the various aspects of the 
message to be expressed by the generator can have 
different degrees of salience, which may give rise 
to certain thematizations and also influence lexical 
choice. Reiter \[1990\] demonstrated the importance 
of basic-level categories (as used by Rosch \[1978\]) for 
generation, overriding the popular heuristic of always 
choosing the most specific word available. 
Generally speaking, the point of "interesting" lan- 
guage generation (that is, more than merely map- 
ping semantic elements one-to-one onto words) is to 
tailor the output to the situation at hand, where 'sit- 
uation' is to be taken in the widest sense, including 
the regional setting, the topic of the discourse, the 
social relationships between discourse participants, 
etc. There is, however, no straightforward one-to- 
one mapping from linguistic features to the param- 
eters that characterize a situation, as, for example, 
stylisticians point out \[Crystal and Davy, 1969\]. Var- 
ious levels of description are needed to account for 
the complex relationships between the intentions of 
the speaker and the variety of situational parameters, 
which together determine the (higher-level) rhetori- 
cal means for accomplishing the speaker's goM(s) and 
then on lower levels their stylistic realizations. 
Here we are interested in the descriptional level 
of lexis: we want to identify linguistic features that 
1 Considerable work has been done on the construc- 
tion of referring expressions, but this is just one specific 
sub-problem of lexical choice, and moreover a context- 
sensitive one. In this paper, we restrict ourselves to 
choice criteria that apply independently of the linguis- 
tic context. 
454 
serve as a basis for choosing a particular lexical item 
from a set of synonyms. Not all these features are 
equally interesting, however; as Crystal and Davy 
\[1969\] noted, the relation between situational fea- 
tures and linguistic features is on a scale from to- 
tal predictability to considerable freedom of choice. 
Among the less interesting dimensions are dialect 
and genre (sub-languages pertaining to particular do- 
mains, for example legal language or sports talk), 
because they tend to merely fix a subset of the vo- 
cabulary instead of Mlowing for variation: the fact 
that what Americans call a lightning rod is a light- 
ning conductor in British English does not imply a 
meaningful (in particular, not a goal-directed) choice 
for a speaker; one rarely switches to some dialect for 
a particular purpose. More interesting is the degree 
of semantic specificity of lexical items. An example 
from Cruse \[1986\]: see is a general term for hav- 
ing a visual experience, but there is a wide range 
of more specific verbs that convey additional mean- 
ing; for instance, watch is used when one pays atten- 
tion to a changing or a potentially changing visual 
stimulus, whereas look at implies that the stimulus is 
static. Such subtle semantic distinctions demand a 
fine-grained knowledge representation if a generator 
is expected to make these choices \[DiMarco et ai., 
1993\]. 
An important factor in lexical choice are collo- 
calionai constraints stating that certain words can 
co-occur whereas others cannot. For instance, we 
find rancid butter, putrid fish, and addled eggs, but 
no alternative combination, although the adjectives 
mean very much the same thing. 2 Collocations hold 
among lexemes, as opposed to underlying semantic 
concepts, and hence have to be represented as lexicai 
relations. They create the problem that individual 
lexical choices for parts of the semantic representa- 
tion may not be independent: roughly speaking, the 
choice of word x for concept a can enforce the choice 
of word y for concept b. 
Finally, a highly influential, though not yet very 
well-understood, factor in lexical choice is style. 
3 Lexical Style 
The notion of style is most commonly associated with 
literary theory, but that perspective is not suitable 
for our purposes here. Style has also been inves- 
tigated from a linguistic perspective (e.g., Sanders 
\[1973\]), and recently a computational treatment has 
been proposed by DiMarco and Hirst \[1993\]. What, 
then, is style? Like Sanders, we view it broadly as 
the choice between the various ways of expressing 
the same message. Linguists interested in style, as, 
for instance, Crystal and Davy \[1969\], have analyzed 
the relationships between situational parameters (in 
2In NLG, collocation knowledge has been employed 
by, inter alia, Smadja and McKeown \[1991\] and Iordan- 
skaja, Kittredge and Polgu~re \[1991\]. 
particular, different genres) and stylistic choice, and 
work in artificial intelligence has added the impor- 
tant aspect of (indirectly) linking linguistic choices 
to the intentions of a speaker \[Hovy, 1988\]. Clearly, 
the difficult part of the definition given above is to 
draw the line between message and style: what parts 
of an utterance are to be attributed to its invariant 
content, and what belongs to the chosen mode of 
expressing that content? 
In order to approach this question for the level 
of lexis, hence to investigate iezicai style, it helps 
to turn the question "What criteria do we employ 
for word choice?" around and to start by analyz- 
ing what different words the language provides to 
say roughly the same thing, for example with the 
help of thesauri. By contrastively comparing simi- 
lar words, their differences can be pinned down, and 
appropriate features can be chosen to characterize 
them. A second resource besides the thesaurus are 
guidebooks on "how to write" (especially in foreign- 
language teaching), which occasionally attempt to 
explain differences between similar words or propose 
categories of words with a certain "colour" (cf. \[Di- 
Marco et ai., 1993\]). One problem here is to deter- 
mine when different suggested categories are in fact 
the same (e.g., what one text calls a 'vivid' word is 
labelled 'concrete' in another). 
An investigation of lexical style should therefore 
look for sufficiently general features: those that can 
be found again and again when analyzing differ- 
ent sets of synonymous words. It is important to 
separate stylistic features from semantic ones, cf. 
the choice criterion of semantic specificity mentioned 
above. The whole range of phenomena that have 
been labelled as associative meaning (or as one as- 
pect under the even more fuzzy heading connotation) 
has to be excluded from this search for features. For 
example, the different overtones of the largely syn- 
onymous words smile, grin (showing teeth), simper 
(silly, affected), smirk (conceit, self-satisfaction) do 
not qualify as recurring stylistic features. Similarly, 
a sentence like Be a man, my son/alludes to aspects 
of meaning that are clearly beyond the standard 'def- 
inition' of man (human being of male sex) but again 
should not be classified as stylistic. And as a final 
illustration, lexicM style should not be put in charge 
to explain the anomaly in The lady held a white lily 
in her delicate fist, which from a 'purely' semantic 
viewpoint should be all right (with fist being defined 
as closed hand). 
Stylistic features can be isolated by carefully com- 
paring words within a set of synonyms, from which a 
generator is supposed to make a lexical choice. Once 
a feature has been selected, the words can be ranked 
on a corresponding numerical scale; the experiments 
so far have shown that a range from 0 to 3 is sufficient 
to represent the differences. Several features, how- 
ever, have an 'opposite end' and a neutral position 
in the middle; here, the scale is -3... 3. 
455 
Ranking words is best being done by construct- 
ing a "minimal" context for a paradigm of synonyms 
so that the semantic influence exerted by the sur- 
rounding words is as small as possible (e.g.: They 
destroyed/annihilated/ruined/razed/.., the building). 
Words can hardly be compared with no context at 
all -- when informants are asked to rate words on a 
particular scale, they typically respond with a ques- 
tion like "In what sentence?" immediately. If, on the 
other hand, the context is too specific, i.e., semanti- 
cally loaded, it becomes more difficult to get access 
to the inherent qualities of the particular word in 
question. 
These are the stylistic features that have been de- 
termined by investigating various guides on good 
writing and by analyzing a dozen synonym-sets that 
were compiled from thesauri: 
• FORMALITY: -3...3 
This is the only stylistic dimension that lin- 
guists have thoroughly investigated and that is 
well-known to dictionary users. Words can be 
rated on a scale from 'very formal' via 'collo- 
quial' to 'vulgar' or something similar (e.g., mo- 
tion picture-movie-flick). 
• EUPHEMISM: 0...3 
The euphemism is used in order to avoid the 
"real" word in certain social situations. They 
are frequently found when the topic is strongly 
connected to emotions (death, for example) or 
social taboos (in a washroom, the indicated ac- 
tivity is merely a secondary function of the in- 
stallation). 
• SLANT: -3...3 
A speaker can convey a high or low opinion 
on the subject by using a slanted word: a 
favourable or a pejorative one. Often this in- 
volves metaphor: a word is used that in fact 
denotes a different concept, for example when 
an extremely disliked person is called a rat. But 
the distinction can also be found within sets of 
synonyms, e.g., gentleman vs. jerk. 
• ARCHAIC ... TRENDY: -3... 3 
The archaic word is sometimes called 'obsolete', 
but it is not: old words can be exhumed on pur- 
pose to achieve specific effects, for example by 
calling the pharmacist apothecary. This stylis- 
tic dimension holds not only for content words: 
albeit is the archaic variant of even though. At 
the opposite end is the trendy word that has 
only recently been coined to denote some mod- 
ern concept or to replace an existent word that 
is worn out. 
• FLOPdDITY: -3...3 
This is one of the dimensions suggested by Hovy 
\[1988\]. A more flowery expression for consider 
is entertain the thought. At the opposite end 
of the scale is the trite word. Floridity is occa- 
sionally identified with high formality, but the 
two should be distinguished: The flowery word 
is used when the speaker wants to sound im- 
pressively "bookish", whereas the formal word 
is "very correct". Thus, the trite house can be 
called habitation to add sophistication, but that 
would not be merely 'formal'. Another reason 
for keeping the two distinct is the opposite end 
of the scale: a non-flowery word is not the same 
as a slang term. 
• ABSTRACTNESS: -3...3 
Writing-guidebooks often recommend to replace 
the abstract with the concrete word that evokes 
a more vivid mental image in the hearer. But 
what most examples found in the literature re- 
ally do is to recommend semantically more spe- 
cific words (e.g., replace to fly with to float or 
to glide), which add traits of meaning and are 
therefore not always interchangeable; thus the 
choice is not merely stylistic. A more suitable 
example is to characterize an unemployed person 
(abstract) as out of work (concrete). 
• FORCE: 0...3 
Some words are more forceful, or "stronger" 
than others, for instance destroy vs. annihilate, 
or big vs. monstrous. 
There is an interesting relationship (that should 
be investigated more thoroughly) between these fea- 
tures and the notion of core vocabulary as it is known 
in applied linguistics. Carter \[1987\] characterizes 
core words as having the following properties: they 
often have clear antonyms (big--small); they have a 
wide collocational range (fat cheque, fat salary but 
.corpulent cheque, .chubby salary); they often serve 
to define other words in the same lexical set (to beam 
= to smile happily, to smirk = to smile knowingly); 
they do not indicate the genre of discourse to which 
they belong; they do not carry marked connotations 
or associations. This last criterion, the connotational 
neutrality of core words could be measured using 
our stylistic features, with the hypothesis being that 
core words tend to assume the value 0 on the scales. 
However, the coreness of a word is not only a mat- 
ter of style, but also of semantic specificity: Carter 
notes that they are often superordinates, and this 
is also the reason for their role in defining similar 
words, which are, of course, semantically more spe- 
cific. It seems that the notion of core words corre- 
sponds with basic-level categories, which have been 
employed in NLG by Reiter \[1990\], but which had 
originated not in linguistics but in cognitive psychol- 
ogy \[Rosch, 1978\]. 
4 Towards a Model for Lexicalization 
When the input to the generator is some sort of a 
semantic net (and possibly additional pragmatic pa- 
rameters), lexical items are sought that express all 
the parts of that net and that can be combined into a 
grammatical sentence. The hard constraint on which 
456 
(content) words can participate in the sentence is 
that they have the right meaning, i.e., they correctly 
express some aspect of the semantic specification. 
The second constraint is that collocations are not to 
be violated, to avoid the production of a phrase like 
addled butter. The other factors mentioned above en- 
ter the game as preferences, because their complete 
achievement cannot be guaranteed -- if we want to 
speak 'formally', we can try to find particularly for- 
mal words for the concepts to be expressed; but if 
the dictionary does not offer any, we have to be con- 
tent with more 'standard' words, at least for some of 
the concepts underlying the sentence. We can max- 
imize the achievement of lexical-stylistic goals, but 
not strive to fully achieve them. 
To arrive at this kind of elaborate lexical choice, I 
first employ a iexical option finder (following ideas 
by Miezitis \[1988\]) that scans the input semantic 
net and produces all the lexical items that are se- 
mantically (or truth-conditionally) appropriate for 
expressing parts of the net. If the set of options con- 
tains more than one item for the same sub-net, these 
items can differ either semantically (be more or less 
specific) or connotationally (have different stylistic 
features associated with them). 
The second task is to choose from this pool a set 
of lexical items that together express the complete 
net, respect collocational constraints (if any are in- 
volved), and are maximal under a preference func- 
tion that determines the degree of appropriateness 
of items in terms of their stylistic and other conno- 
tational features. Finally, the choice process has to 
be integrated with the other decisions to be made in 
generation (sentence scope and structure, theme con- 
trol, use of conjunctions and cue words, etc.), such 
that syntactic constraints are respected. 
Two parts of the overall system have been realized 
so far. First, a lexical option finder was built with 
LOOM, a KL-ONE dialect. Lexical items correspond 
to configurations of concepts and roles (not just to 
single concepts, as it is usually done in generation), 
and the option finder determines the set of all items 
that can cover a part of the input proposition (repre- 
sented as LOOM instances). Using inheritance, the 
most specific as well as the appropriate more general 
items are retrieved (e.g., if the event in the proposi- 
tion is darning a sock, the items darn, mend, fix are 
produced for expressing the action). 
5 Stylistic Lexical Choice in 
PENMAN 
At the 'front end' of the overall system, a lexical 
choice process based on the stylistic features listed 
in section 3 has been implemented using the PEN- 
MAN sentence generator \[Penman-Group, 1989\]. 
Its systemic-functional grammar has been extended 
with systems that determine the desired stylistic 
"colour" and, with the help of a distance metric (see 
below), determine the most appropriate lexical items 
that fit the target specification. 
Figure 1 shows a sample run of the system, where 
the :lexstyle keyword is in charge of the variation; 
its filler (here, slang or newspaper) is being trans- 
lated into a configuration of values for the stylistic 
features. This is handled by the standard mech- 
anism in PENMAN that associates keyword-fillers 
with answers to inquiries posed by the grammatical 
systems. In the example, the keyword governs the 
selection from the synonym-sets for evict, destroy, 
and building (stored in Penman's lexicon with their 
stylistic features). The chosen transformation of the 
:lexstyle filler into feature values is merely a first 
step towards providing a link from low-level features 
to more abstract parameters; a thorough specifica- 
tion of these parameters and their correspondence 
with lexical features has not been done yet. 
More specifically, for every stylistic dimension one 
system is in charge to determine its numeric target 
value (on the scale -3 to 3). Therefore, the par- 
ticular :lexstyle filler translates into a set of fea- 
ture/value pairs. When all the value-inquiries have 
been made, the subsequent system in the grammar 
looks up the words associated with the concept to be 
expressed and determines the one that best matches 
the desired feature/value-specification. For every 
word, the distance metric adds the squares of the 
differences between the target feature value (tf) and 
the value found in the lexical entry (wf) for each of 
the n features: ~i~=l(tfi - wfi) 2 
The fine-tuning of the distance-metric is subject to 
experimentation; in the version shown, the motiva- 
tion for taking the square of the difference is to, for 
example, favour a word that differs in two dimen- 
sions by one point over another one that differs in 
one dimension by two points (they would otherwise 
be equivalent). The word with the lowest total dif- 
ference is chosen; in case of conflict, a random choice 
is made. 
6 Summary and Future Work 
An important task in language generation is to 
choose the words that most adequately fit into the ut- 
terance situation and serve to express the intentions 
of the speaker. I have listed a number of criteria for 
lexical choice and then explored stylistic dimensions 
in more detail: Arguing in favour of a 'data-driven' 
approach, sets of synonyms have been extracted from 
thesauri and dictionaries; comparing them led to a 
proposed set of features that can discriminate syn- 
onyms on stylistic grounds. The features chosen in 
the implementation have been selected solely on the 
basis of the author's intuitions (albeit using a sys- 
tematic method) -- clearly, these findings have to be 
validated through psychological experiments (asking 
subjects to compare words and rate them on appro- 
priate scales). Also, it needs to be explored in more 
detail whether different parts of speech should be 
457 
(say-spl '(rr / rst-sequence 
:domain (d / EVICT :actor (p / PERSON :name tom) 
:actee (t / TENANT :determiner the :number plural) 
:tense past :lexstyle slang) 
:range (e / DESTROY :actor p 
:actee (b / BUILDING :determiner the) 
:tense past :lexstyle slang))) 
"Tom threw the tenants out, then he pulverized the shed." 
(say-spl '(rr / rst-sequence 
< same as above > 
:tense past :lexstyle newspaper))) 
"Tom evicted the tenants, then he tore the building down." 
Figure h Sample run of style-enhanced PENMAN 
characterized by different feature sets. 
An overall model of lexicalization in the generation 
process has been sketched that first determines all 
candidate lexical items for expressing parts of a mes- 
sage (including all synonyms and less-specific items), 
and a preferential choice process is supposed to make 
the selections. The front-end of this system has been 
implemented by extending the PENMAN sentence 
generator so that it can choose words on the basis of 
a distance function that compares the feature/value 
pairs of lexical entries (of synonyms) with a target 
specification. This target specification has so far only 
been postulated as corresponding to various stereo- 
typical genres, the name of which is a part of the 
input specification to PENMAN. In future work, the 
stylistic features need to be linked more systemati- 
cally to rhetorical goals of the speaker and to param- 
eters characterizing the utterance situation. One of 
the tasks here is to determine which features should 
be valid for the whole text to be generated (e.g., for- 
mality), or only for single sentences, or only for single 
constituents (e.g., slant). 
Besides, ultimately the work on lexical style has 
to be integrated with efforts on syntactic style \[Di- 
Marco and Hirst, 1993\]. Other criteria for lexical 
choice, like those mentioned in section two, have to 
be incorporated into the choice process. And finally, 
it has to be examined how lexical decisions interact 
with the other decisions to be made in the gener- 
ation process (sentence scope and structure, theme 
control, use of conjunctions and cue words, etc.). 
Acknowledgements 
Financial support from the Natural Sciences and En- 
gineering Research Council of Canada and the Infor- 
mation Technology Research Centre of Ontario is ac- 
knowledged. Part of the work reported in this paper 
originated during a visit to the Information Sciences 
Institute (ISI) at the University of Southern Califor- 
nia; thanks to Eduard Hovy for hospitality and in- 
spiration. For helpful comments on earlier versions 
of this paper, I thank Graeme ttirst and two anony- 
mous reviewers. 
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