Controlling Lexical Substitution in Computer Text Generation 1 
Robert Granville 
MIT Laboratory for Computer Science 
545 Technology Square 
Cambridge, Massachusetts 02139 
Abstract 
Th=s report describes Paul, a computer text generation system 
desig~ed LO create cohesive text through the use o| lexlcal substitutions. 
Specihcally, Ihas system is designed Io determmistically choose between 
provluminahzat0on, superordinate suhstntut0on, and dehmte noun phrase 
reiterabon. The system identities a strength el antecedence recovery for 
each of the lex~cal subshtutions, and matches them against the strength 
el potenfml antecedence of each element m the text to select the proper 
substitutions for these elements. 
1. Introduction 
This report descrnbes Paul. a computer text generation system 
designed to cre~:te collesive text through tile use of lexical substitutuons. 
Spec;hcalty. thts system ~s designed tn deterministically choose between 
pronominal:zabon sup(:rordinate substitution, and delinite noun phrase 
reitcrat}on. Fl~e system identifies a strength at antecedence recovery for 
each of the lexical substitutions, anti matches them against the strength 
of potenU,# entececJence of each element =n the text to select the proper 
sub3litubons for these elements. 
P~ul is a natural language generation program initially developed at 
IBM's Thomas J. Watson Research Center as part of the ongoing Epistle 
project I5.6}, "\[he emphasis of the the work reported here is in the 
research oJ discourse phenomena, the study of cohesion and its effects 
on mLJlhsententiat texts \[3, 9\]. Paul accepts as input LISP knowledge 
structures consisbng of case frame l1\] formalisms representing each 
sentence to be gernerated. These knowledge structures are translated into 
Enghsh, with the appropriate lexical substitutions being made at this time. 
No attempt vs made by the system to create these knowledge structures. 
2. Cohesion 
The purpose of communication is for one person (the speaker or 
writer) to express her thoughts and ideas so that another (the listener or 
reader) can understand them. \]here aJe many restrictions placed on the 
realization of these thoughts inio language so that the listener may 
understand. One ot the most important requiroments fo~ an utterance is 
that it seem to be unified, that it form a text. The theory of text and what 
distinguishes it from isolated sentences that is used in Paul is that of 
Halliday and Hasan \[3\]. 
One of the items that enhances the unity of text is cohesion. 
Cohesion refers to the linguistic phenomena that establish relationships 
between sentences, thc~reby tying them together. There are two major 
goals that are accomplished tl~rougi~ cohesiu, that enhance a passage's 
qualily of text. The fiust is the obwous oesure to avoid unnecessary 
repetibon. The other goal is to dislinguL",h new information from old. ,so 
that the listener can tully undemtand what fs being said. 
\[1} The room has a large window, The room has a window 
facing east. 
{1} appears to he describing two windows, because there is no 
device indicating that the window of the second sentence is the same as 
the window of tile first sentence. If in tact the speaker me:mr to describe 
the stone w;ndow, silo must somehow inform the listener that this is 
1This research was s.pported (in part) by Office of Naval Research contract 
NO0 14-80-C.0505, anJ (in pint) by Nation31 Institutes of I-le31lh Grant No. 1 POt LM 
03374.04 from the National Library of Medicine. 
indeed the case. Cohesion us a device that will accomplish thas goal, 
Cohesion is created when the interpretation of an element is 
dependent on the me.aning of another. \]he element in guestion can.at be 
hJIly understood until 1he element d is dependenl on zs ~dcntdned. rhe first 
presupposes \[3\] the second in that it requ,es for its understanding the 
exnstence of the second. An element at a sentence presupposes the 
existence of another when its interpretation requires relerence tO 
another. Once we can trace these lelerences to their sources, we can 
correctly interpret the elements of the sentences. 
The very same devices that create these depende, leies for 
interpretation help distinguish olct intolrnation from new. I\[ the use of a 
cohesive element pre~.upposes the exnste~ce of another role=once el the 
element lor its ir}terpretahon, tl~en tile hstener can be assured tltat the 
olher reference exists, and that the element =n question can be 
understood as old reformation. lhurefore, that act at associating 
seJltences through reference deponde.cies heips make the text 
unambiguous, arid cohcs=on can be seen to be a very important part of 
text. 
3. Lexical Substitution 
In \[3\], Halliday and I-lasan cat.~log and discuss many devices used 
in English to acmove cohes,on. Fhese include refe;ence, substitution 
ellaDsis, and conjunction. Another f.t, mily ut devices they discuss is know,-" 
as lexical substitulion. \]he lexlcal substitution devices incorporated into 
Paul are pronommalizatior,, s.perordinate substitution, and definite noun 
phrase reiteration. 
Superordinate substitution is the replacement of an etement with a 
noun or phrase that ps a .;ore general term for the element As an 
example, consPder Figure 1, a sample hierarchy the system uses to 
generate sentences. 
................................................. 
ANIMAL 
MAMMAL REPT ILE i 
POSSUM SKUNK TURTLE 
I I r 
POGO HEPZIBAH CHURCHY 
Ftgure la 
................................................. 
1, POGO IS A HALE POSSUM. 
2. HEPZIBAH IS A FEMAt.\[ SKUNK. 
3. CItURCHY IS A M~LE TURTLE. 
4. POSSUMS ARE SHALL, GREY MAMMALS. 
5, SKUNKS ARE SMALL, BLACK MAMMALS. 
6. TURILES ARE SMALL, GREEN REPTILES, 
7. MAMMALS ARE FURRY ANIMALS. 
B, REPTILES ARE SCALED ANIMALS, 
Figure Ib: A S~mple llierarchy for Paul 
381 
It1 Ih~s t!x:lrh;.~l(~, lap SIJ|)(!roI(JlE~;.aIO of POds() I~ POS~l.lf.f, that of 
PO~';S{IM ~s MAM~.IAI. aMd ,~,;jain for M,lt/MAI the supo~ordmate is 
A^#IMAI Suporord,natet; c,,t;n contraLtO for as long as the h~erarchical tree 
will s~ppor t. 
The n,echanlct~ Io, performing superord~nate substdutio:'~ is fairly 
{,asy. All ,)+~e no(nil,; tO Of: ;S t++ t'l++;~tO, a list C}t s+q'~++ior,flllm~!:~ try tr~ICllSg up 
the hi~:rarch+cal bet!. an~J Cub~l;,,rfly c l~(;ose It(,i;x C!},s list. t Iowever. lhere 
are sev(:l,d i~\[;uob that IrlUbI I,e dddrr;sbcrJ to prevcllt s;,perorjir~ate 
SUbStitutIOn florrl hell"i{j alll~)lgtlL)llS or rY!n,,,,ln{j ('lloneous CO;HK)tatiOrlS. 
The etrofle(Als CO~H)otatlunS ~'CCLI r It Ih(~ h:';t O! L;upelordlnL~,lu+. t i~% allowed 
to extot+d too long An ex:lmpIn will t:l,+kc ih;:4 c:ltLff. Let us ;\]£~umo that we 
have a h~C'ralchy in wn=++t'~ th+,le is ar~ (:~drv ! Hi It. ll'le superor'dlnate Of 
\[t~ED iS MAf4. t~Jf A,I,It# t'/t}t,t,,'~N. ANIMAL for tlfJM.'~IV. :rod rilING for 
ANIM,1L. fhorefore, the superordu,ate hsl for hR~.D ~s IMAN tlUMAN 
AHIM4L THINGS. Whilo retenin{I to frcd as llle man seems fmc, calling 
h~m the ,~tuman seems a Iitl=e z, tran{je. And lurtherlF~ore, using the animal 
o+ + the thing to refer to Fred ~s actually insulting. 
\]+he reason these superordinates have negative connalations is 
that there are e~sentKd quahttes that hH+;rans p,':,ssess that s,+p~;rate ,is 
from ell;or animals. Calhug FrEd an "anlIi;id" m+1111es that he lai-ks tar,so 
quahhea, al\]:.f is tt;oreiore insulhog. "l.h+man" sotJnds change because it 
is the hvihest e=rlry in the seln~mtic hterlrrchy that exhibits these qualities. 
lalk,:g about "the humnr~" tl~ves erie the feeling that there are other 
creatules in the d=scourse that aren't human. 
Paul is senmtive to the connotabons that are possible Ihrough 
superordinate substitubon. The+ system tdeobfies an es;~e+~tial quality, 
usu\[\]liy ir=telligence, wilich acts as a block for further supurordinate 
subsbtution. If the item to be replaced with a superordmate has the 
prou.~rty of intelhgence, either d~reclly or through semantic inheritance, a 
superordinate list is made only {)f tho..:e entnes that have themselves the 
quality el intothgenco, a{j.qir, either d~rectly or through inheritance. If the 
item does=rt have intelhgence the list is allowed to extend as far as the 
hierarcl~ical entries will allow. Once the proper list of superordinates =3 
established, Paul randomly chooses one, preventing repetition by 
remembering previous choices. 
The other problem with superordinato substitution is that it may 
=ntroduce ambiguity. Again cons=tier Figure 1. If we wanted to perform a 
superord.\]ato subshhlho;+ for POrJO. we would have the sup~'rordJt13te 
hst (POSSUM MAMMAL ANIM4L ) to choose from. But HEPZlI\]AH is also a 
nlammal, so the rnammal cauld refer to either POGO or HEPZIBAH. And 
not only are both POGO e,r}d ItEPZIBAtl anunals, but sn is CtlURCHY, so 
the armnat could be any o,}e of them. \]herefore, saying lhe matnmal or 
the arr+mal would form an ambiguous refecence which the listener or 
reader would have rio v,,,ay to ur~derstand. 
Paul reco{.lniz££ \[hts ambiguity. Once the superordinate has been 
selected, it ~s tested against all the other nour~s mentioned so far in the 
text If any other noun is a rn{;mbet of th.e superordu+ale set m question, 
the reference is ambl,~!uous. 1his reference can be disarnbiguated by 
using some feature ot the eh:ment be,to replaced as a modilier. In our 
example of Figure 1. we hrd that all possums are grey. and therefore 
POGO ~s grey. Thus. the grey mamma! can refer only to POGO, and is not 
atnb=guous. In the Pogo world, the features the system uses to 
d~sarr;oiuuate these references are gender, s~ze, color, and skin type 
(furry. scaled, of foath{,~('d). Or+co the leature ~s arb~trC.rily selected and 
the correct value has been determined. ~t ~s tested to see that it genuinely 
diba+nb~guales the reference, tt any of the nouns that were members of 
the :,t;pcrordmate set have the same value to~ this feature, it cannot be 
use,') to (f~s.~mb~guate the reference, arid il is relected. For instance, tl~e 
size of POGO ~s small, but s~ying the .~n',all mammal ~3 still ambiguous 
bec~use HEPZll~Atl is also small, and the phrase could just as likely refer 
to her. The search for a disambiguatmg ieature continues until one is 
found. 
Pronominalizat+on, the use of personal pronouns in place of an 
element, is mechan~c~dly simple. The selecbon of the appropriate 
persnnal pronoun is strictly gramm;-~lical. Once lhe syntactic case, the 
oendor, and the number of the element are known, the correct pronoun is 
dictated by the language. 
the final ~ex~cal substitution available to Paul is the definite noun 
phrase, the use of a dehnite artielr~, t,'~e m English, as opposed to an 
indefinlle article, a or some The definite ~rticle clearly marks an item as 
erie that has been pre,~iously mentioned, and is therefore old information. 
"f:',e .'~rlefu,te oracle 31mllatiy marks an item as not havlnq been 
pre..qc~usiy mentioned. ,~d therefore is new information. 1"his capacity of 
the defimte article makes ils use required with superordinates. 
{2} My collie is smart. The dog fetches my newspaper every day. 
"My collie is smart. A dog fetches my newspaper every day. 
Willie the mocharlisms for performing the various lexical 
substitutions are conceptualiy slra~ghtforward, they don't solve the entire 
problotn uf usin~.l le,:icdl suOstltuhon. Nolhing has been said about how 
the system chooses WlllCh IOxICUl substilutlor'i to use. This is a serious 
issue because lexlcGI sLJbsbtutiol~ dOWCOS ace nc;t interchangeable. This is 
tru.,3, bec;~u:;e le~Jcal substiluhons, as Wltll most cohesive devices, create 
text by using pze:;uppo-~t;d dependencies tor Iheir inlerpreti'|tioi1s, as we 
have seeri. If those pr£~Supposod elemeats do not exist, or if it is not 
possible to Correctly idcnhly whtch of the m~'.ny possiDle elements is the 
one presuppns,.xi, then it is imoossiblo to correctly int(,rpret the element, 
arid the only possd.)le r¢su!t ~s cunlus~on. A computer text generation 
symptom mat incorporates lexical substituhon in its output must insure that 
tne presupposed element ex:sts, and that it can be readily identified by 
the reader. 
Pa~d controls the se!ection of lexicai substitution devices by 
conceptually dividing the p+ helen rote two I'.,sks. "rho first is to ~dentify the 
strength of antecedence rucov'crv of toO lexical substitution devices. The 
second ~s to iderztffy the str~..ngth el pote~:hal arrteceder~ce of each 
element in the passage, and determine which il any Icxical substitution 
would be appropriate. 
4. Strength of Antecedence Recovery 
Each time a cohesive devic~ is used, a presupposition clependency 
is created. rhe itef~ tIlat i:; being presupposed must be correctly 
identified tor the correct interp~etabon of the element. The relative ease 
with wh=ch one c3n recover this pre~supposed item from the cohesive 
element is called the strength el antecedence recove,y. The stronger an 
eleraent's strength of antecedence recovery, the easier it is to identify the 
presupposed element. 
The lexical substitution with the highest strength of antece-lonce 
recovery is the dehnite noun. This is because the element is actually a 
recetition of the original item, w~th a definite article to mark the fact that it 
is old information. There is no real need to refer to the presupposed 
element, since all the reformation is being repeated. 
Superordinate subslitution is the lexical substitution witl; the next 
highest strength of antecedence recovery. Presupposition oepondency 
genuinely does ernst with Ihe use of superordmates, because some 
intorrnation is lost When w* ~. move up the semanhc hierarchy, all the traits 
that are specihc to the element in question are test. To recover this and 
fully understand the ret(;rence at Ilano. we must trace back to the original 
element in the hierarchy. Fortunately, the manner in which Paul pedorms 
suporordmate substitution faohtates this recovery. By insunng that the 
superordmate substitt;tlon will never be ambiguous, the system only 
generates suporofdmate ~L, bstttutlons that are readily recoverable. 
The th,d device used by Paul. ~he personal pronoun, has the lowest 
strength of antecedence recovery. Pronouns genuinely ~re nothing more 
tharl plat:e holders, variables that lea=tHole the pnsihotls Of the elements 
they are replacing A pronoun contains no real semahhc irdormation. The 
only readily available p~eces of iniormation from a pronoun are the 
syntactic role Jn the currenl sentence, the gender, and the number of the 
replaced item. For this mason, pronouns are the hardest to recover of the 
substitutions discussed. 
5. Strength of Potential Antecedence 
Wl~tle the forms of lexical substitution provide clues (tO various 
degrees) teat aid the reader in recovering the presupposed elemeflt, the 
actual way m which the e!orr;er;t =S currerttly being used, how ;t was 
prev;:)usly used. its cir,,:um,~ tances within the current sentence and within 
the eqt~re text, can prowce addit;on31 clues. These factors combine to 
give tne 5pecIhc reference a s~ret;gth el potentiat antecedence. Some 
etemer~ts, try the ;,ature of their current and previous us~.~ge, will be easier 
to recover u;depetl~ont of u~e fox,cat subst~lutton dewce selected. 
Strength of potential antecedence involves several factors, One is 
the syntachc role the element ~s pl~ying in tr}e current sentence, as well 
as in the previous relere;ice. Anoti~er is the d~stance of the previous 
reference from the current. Here distance is defined as the number of 
clauses between the references, and Paul arbitrarily uses a distance of no 
more than two clauses as an acceptable distance. The current expected 
382 
focus of the text also affects an element's potential strength of 
antecedence. In order to identify the current expected locus, Paul uses 
the detailed algorithm for focus developed by Sidner \[10\]. 
Paul identifies five classes of potenhal antecedence strength. Class 
I being the strongest and Class V the weakest, as well as a sixth "non- 
class" for elements being mentioned for the first time. These five classes 
are shown in Figure 2. 
Class h 
1. The sole referent of a given gender and number (singular or 
plural) last menbo~lod within an acceptable distance. OR 
2. The locus or the head of the expected locus list for the previous 
sentence. 
Class Ih 
The last relerent el a g=ven gender and number last mentioned 
w;thin an acceptable distance. 
Class IIh 
An element that filled the same syntactic role in the previous 
sentence. 
Class IV: 
1. A referent that has been previously mentioned, OR 
2. A referent that is a member of a previously mentioned set that has 
been mentioned within an acceptable distance. 
Class V: 
A referent that is known to be a part of a previously mentioned item. 
F~gure 2: The Five Classes of Potential Antecedence 
Once an element's class of potential antecedence is identified, Ihe 
selection of the proper toxical substitubon IS easy. TI~O stronger an 
element's potenbal a~teceder, ce. the weaker the antecedence of the 
lexJcal subslrtutior) I-igule 3 illustrates the mappings lrom potential 
antecedence to lex,c:ll 3ut)stltut~on devices. Note that Class I11 elements 
are unusual i~ that the device used to replace them can vary. If the 
previous instance of the element was of Chtss I. if it was replaced with a 
pronoun, then the Cunent instance =s replaced with a pror~oun, too. 
Othorwh'e, Class III elements are replaced with superordinates, the same 
as Class I1. 
Class I ...................... Pronoun Substitution 
Class II ............... Superordinate Substitution 
Class Ill (previous reference Class I) 
................... Pronoun Substitution 
Class III .............. Superordinate Substitution 
Class IV ..................... Definite Noun Phrase 
Class V ...................... Definite Noun Phrase 
Figure 3: Happing of Potential Antecedence 
Classes to Lexical Substitutions 
6. An Example 
To see the effects of controlled lexical substitution, and to help 
clarify the ideas discussed, an example is provided. The following is an 
actual example of text generated by Paul Tile domain is the so-called 
children's story, and the example discussed here is one about characters 
frorn Walt Kelly's Pogo comic strip, as shown in Figure 1 above. 
Figure 4 contains the semantic representation for the example story 
to be generated, in the syntax of NL P \[4\] records. P 
al('like'.exp:='a2',recip:='a3',stative); 
aZ('pogo'); 
a3('hepzibah'); 
bt('tike',exp:='b2',recip:='a3'0staLive); 
b2('churchy'); 
cl('give',agnt:='aZ',aff:='cZ',rectp:='a3', 
active,effect:='c3'); 
c2('rose'); 
c3('enjoy\'.recip:='a3',stative); 
dl('want\',exp:='a3',recip:='d2',neg,stative); 
d2('rose',pussess:='b2'); 
e1('b2',char:='jeatous'.entity); 
f1('hit\',agnt:='b2'.aff:='a2'.active); 
gl('give',agnt:='b2',aff:='g2', 
recip:='a3',ective); 
gZ('rose'); 
hl('drop\',exp:='h2',stative); 
h2('petal',partof:='g2',plur): 
il('upset\',recip:='a3',cause:='hl',stetlve): 
j)('cry\',agnt:='a3',active)\[\] 
Figure 4: NLP Records for Example Story 
................................................. 
If the SIOFy were to be generated without any lexical subslitutions at all, it 
would look like the following. 
POGO CARES FOR HEPZIBAH. CHURCHY LIKES HEPZIBAH, 
TOO. POGO GIVES A ROSE TO HEPZIBAH, WHICH PLEASES 
HEPZIBAH. HEPZIBAH DOES NOT WANT CHURCHY'S ROSE. 
CHURCHY IS JEALOUS. CHURCHY HITS POGO. CHURCHY 
GIVES A ROSE TO HEPZIBAH. PETALS DROP OFF. THIS 
UPSETS HEPZIBAH. HEPZIBAH CRIES. 
While this version of the story would be unacceptable as tile final product 
of a text generator', and it is not the text Paul would produce from the 
input of Figure 4. it is shown here so that the reader can more easily 
understand the story reiJrosonted semantically in Figure 4. 
To go to the nther extreme, uncontrolled pronominalization would 
be at least a~ unacceptable as no Icxicai subslihJtions at all. 
POGO LIKES HEPZlBAH. CHURCHY CARES FOR HER, TOO. 
HE GIVES A ROSE TO tIER. WHICH PLEASES HER. SHE 
DOES NOT WANT HIS ROSE. HE IS JEALOUS. HE SLUGS 
HIM. HE GIVES A ROSE TO HER. PETALS DROP OFF. 
THIS UPSETS HER. SHE CRIES. 
Again. this is unacceptable text. and the system would not generate it, but 
it is shown hero to dramatize the need for control over lexical 
substitutions. 
Tile text that Paul actually does produce from the input of Figure 4 
is the following story. 
POGO CARES FOR HEPZII3AH. CHURCHY LIKES HER, TOO. 
POGO GIVES A ROSE TO HER, WHICH PLEASES HER. SHE 
DOES NOT WANT CHURCI-IY "S ROSE. HE IS JEALOUS. I.IE 
PUNCHES POGO. FIE GIVES A ROSE l'O itEPZIBAH. THE 
PETALS DROP OFF. THIS UPSETS HER. SHE CRIES. 
2For a discus~on of the imptornentalion el NI.P for Paul .~e \[2\]. 
383 
7. Conclusions 
The need for good te,~:t generation is rapidly increasing. One 
requirement for generated Output to be Cor'.~idored text is to exhibit 
cohesion I.ex~cal substiluhon ~S a family of cohesive devices that help 
p~ow(te coho:;~on and achtew~ the two mater goals of cohesion, the 
avoLdmg of unnecussary repet=t=on and the d=shnguishing of old 
inlormat~on from new. Ftowovor. uncontrolled use of lexicai substitution 
dewces wdl prodHce texl thai is t,n~ntelhgible and nonsensical. P~'~ul is Ihe 
first text genehltlr~n syslet:, tn,II Incorporates Iox~oai substiluhon8 in a 
controlled mantlet, tnereby producing COhesive text that is 
~,;rJorstandal)le By ~dentify\]n0 the L;trurlgth Of antecedence recovery for 
each of the lexical subslitutJor~s, and the strength of potential 
antecedence for each element i~ the discourse, the syslom i$ able to 
choose the app,'opnate lexical substitutions. 
8. Acknowledgments 
t would like to thank Pete SLolovits and Bob Berwick for their advice 
and encoura,aen;ent while suporvisu}g this work. I would also like to thank 
Geor,jo t ieidorn and Karon Jensen for or~'!inc~lly introducing me to the 
problem addressed here, as well as their expert help at the ec, rly stages of 
this project. 
9. References 
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Tlleory. Emmon Bach and Robert T. Harms, Ed., Holt, Rinehart and 
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2. Granville, Robert Alan. Cohesion in Computer Text Generation: 
Lexical Substitution. Tech. Rcp. MIT/LCS/TR-310, MIT,Cambridge, 
1983. 
3. Halliday, M. A. K., and Ruquaiya Hasan. Cohesion in English. 
Lon§mar~ Group Limited, London, 1976. 
4. Heidorn, George E. Natural Language Inputs to a Simulation 
Programming System. Tech. Rep. NPS-551 ID72101 A, Naval Postgraduate 
School, Monterey, Cal., 1972. 
5. l'teidorn, G. E., K. Jensen, L. A. Miller, R. J. Byrd, and M. S. Chodorow. 
The Epistle Text-Critiquing System. IBM Systems Journal 21, 3 (1982). 
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Compuler GeneTahon of Topic Paragraphs: Structure and Style. 
Proceedings o1 the 19th Annual Meeting of the Association for 
Cornputahonal Linguistics, Association for Computational Linguistics, 
1981. 
8. Mann. William C., Madeline Bates, Barbara J. Grosz, David 
D. McDonald. Kathleen R. McKeown. and William R. Swartout. Text 
Generation: The State of the Art and the Literature. Tech. Rep. ISI/RR. 
81 .t01, information Sciences Institute, Marina del Rey, Cal., 1981. Also 
University of Pennsylvania MS-CIS-81-9. 
9. Quirk, ,~andolph, Sidney Greenbaum. Geoffrey Leech, and Jan 
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MI r, Cambridge, 1979. 
384 
