A SET-THmRErlC APPROACH TO LEXICAL 
~DLffO1T 
MEMODATA 
23 rmdesaoufi~ 
14000 Ca~ Frm~ 
RESUME 
UNE APPRO~tE ENSEMBLtSIE DU ~ ~ 
r,bus pr~ntms ~ ~a~ts d'm travan mm~ depuis 
ltt~mrs ~ par la sooi(~ ~DATA stria 
~ s~mmtique du lexique ~ Ce travail a 
fait l'objet d'tm lmm~ ~ & ~ av~ le 
~& h ~a delaT~ et ab(x~ 
attjourd'hui ~ un diaimrtm k~id de #us de ~mmo 
Le fitr¢ "dk~tm~ amiog~" lcgro~ des r6ali~s 
tr~s ~ l~tr nnee part, mus dvitms le mvail 
d~ Sisy~ parm ~ h ~ st~t2p~ poar 
mus omm~ sat h mummkm des dm~ 
s~anfio~ Aini, nnm dassm~ i~ l'aide de 
rat~mr, et selm reapmm hrmi~ ~ rrmm de 
fairs ~ qu~ ~ 6amines & langu~ 
s'amemt ~ rmam et ~ f~r dins rus~ 
um par~ invam~ de l'a~ d~ait d'tme fagon 
fmmi~ 1~ d/akmaire des dmCs l~aux: il est une 
stma~im & mots a d'w~ de n~ pm~t 
l'aspoct d'un gral~ t~ ~ Ixly~. A 
h brae de ¢e gr41,~, nous tmu~ms des etmrbtes 
"pfimitifs ~ : des etmri~ qui n'ont pas & ammam 
at~ que 1~ ~i¢timnaim ~out ml~. A 1'~ exI~ni~ se 
• umt les mo~s ciffmis par les emm/~ aaxqu~s ils 
al~afiena~ ~ir~ que par r~ de leurs 
~o~ians d'~ Dam DIOOLOGIQUE, ks 
mx~s~ d'~ des ena~fl~ 
rfivemx d'tm n,~ae "qmsi-d~ni~" d'un 
mot. I1 exisle 9 types d'eram'~s: 
- Ome ~ "Liste" mmtx~ de nms a~t une 
ap,rala~ de sins et (~ ca~¢~ ~ (nora, 
v,~oe, atje~, ahcrbe). 
-Un ~ "Classe" des~ ~t ~ des 
am'~ims en ~n 0a mologie ~ar ex.). 
-Un msml~e "Tma'es lids", au eontmu assez 
~ de trams n'ay~nt pu dmmr lieu ~ h ca~on 
& lims dam un 'ql't~ dmr,6 
-Un msmt~ q'hi~" cap~ d'~ t~t b 
champ 1odin1 d'um raix Enae ~e, ilpm mmmir 
desthM~ 
- Lrn mmn~ "Descdp~" emli~ m cas de rt~ces~ 
amnitt~ 
- Un msm~ "~" qui ~ des mots 
dont les signifi6s ~mtxma un ~ trot sailk~ 
Les 6nonc6s math~matiques con'estxmd~t it des 
fonctionnalit6s du dictionnaim dlecU'otfique que 
nous avis iUustrfi Par des exemples empnmtfs l~ 
celui-ci : 
-~ de an:errs (veeoes ex#nmt "faire 
mrlzr" et "o~ze', stlm,~ af~mt une "c~e" 
du "Pape", symiymes de "voler" Imur des "alzilles"...). 
- Editim de lis~ ou de lt-,~res ( la lisle "Pem~ atilera 
500,~at~ mca~ ~pttm~ 
- Rtxtztd~ des ~ d'on mot 0e tmne 
~M~tue "aim~ ~ est~ am rare). 
A~c ces ~ emn#es, nous ~ 
~ le diakmmite des chan~ ~xicatx peut are 
consul~ ttitm-~ par on tmqimtmr htmmt Nous 
proms al~ le gdlam de sm ex#/ta~ par h 
n-mhine dbca~. 
En rq~mt ~s cZ~ns ~ du 
dictkm~ mus ~ ~ 1'~ ~amtique 
des th~ d'm petit ~ pare dans la lmme. Nous 
~ ~m sm~ d'ana~ ~ ~caie. 
EI~ est bas6e sat me ~ des etmnt~ 
capables de ceme, ~ sujos ~,ci~s par r~ En 
d~ani~ le diakn~ ix~t Wavainer sat ~ IZ~S temes 
(axts n'~ ~ s~ax~) m-d~as ~ n~ 
dam~,r~prCam,~. 
Actuellement, DICOI_DGIQUE confient plus de 
100000 mots et 15000 ensembles typ~ Les 
350 003 observations d'aptmrtenance dimctes des 
mots, crtk~ par un expert humain, ddveloppmt un 
gtaphe de 4 000 000 de successions d'h6ri-tage que 
nous aa~omns sam cesse. Les outils de base que 
nous ~nsmfisons, tel le SEMIOGRAPHE pour la 
recherche decumentaire, nous Izrme~t d'~valuer 
la progression de la qualit~ des interpr6mtions que 
nous obtemms. 
Nous aaxitms mtre article par norm smfl~ de 
rmm~, ias du 00Llt~ des pamm~ fmOis on 
arang~ qui ~xtraim a~ nous aamger des tm¢~ 
st~ oes question~ 
AcrEs DE COLING-92, NANTES. 23-28 ^ol~'r 1992 9 8 2 PROC. OF COL1NG-92, NANTES, Aua. 23-28, 1992 
A SEI'-~C APPROACTq TO I.EXICAL SEMANTICS 
D.DU101T, MEMODATA. 
~stlRclagrilt~ 
We present the results of the work carried out over 
several years by the Memodata Company on the 
slructure of the French lexicon. This work has been 
accomplished thanks to a first research contract 
with the Ministry of Research and Technology and 
today has lead to a dictionary of more than 100 000 
words and phrases grouped analogically and 
syr~ymo~y. 
If we understand quite well how a dictiolmry like 
this can be used with ease by humans, we set the 
problem of the identification of meaning by a 
computer. We will evaluate how Dicologique adds 
information eomplentaty to the information 
contained in semantic nets. Thanks to a somewhat 
unusual cons~on method and the systematic 
classification of words according to their meaning, 
we are progressing to a continuous system of 
localisalion of the meaning itself. On the map we 
created, it is possible to compute the meaning due 
to lexical semantics for any sentence written in 
natural language... 
1) ~ rt~,,om or ~xXaC_AL 
IRCTIONARH~ 
The purpose of dictionaries grouped under the 
name "analogical" is always the ease of the passage 
from a word to an idea and the inverse passage 
from an idea to a word. This aim is reached by the 
make up of lists of stereotyped associations and of 
semantic fidds. The first aRnoach does not have 
the same likelihood of ending with a satisfactory 
result as the second. 
The stereotyped associations depend on the idea of 
time, the background and the experience of each 
individual. Their record can only be a track of the 
~ve memory from the individual. 
On the other hand, the dictionary of semantic fields 
is perfectly workable at any time ; it is based on the 
linguistic ~x~aventicqrs that the language dictionaries 
have tried for ~nturies to record and to normalize. 
It is not possible, by definition, to consmact a 
dictionary made up of stereotyped ass~ations, 
whereas it is possible to work on the complexity of 
hundreds and t.housar~ of linguistic facts which 
we have classified. 
We will give a mathematical description of the 
dictionary of the semantic fields. This approach is 
in parallel with concrete examples derived from the 
database. 
2) ~ Area ~Tm~ or Tr~ 
"lhe dictionary of analogies and synonyms that has 
been set up is a structure of sets and words which 
the conceptual figure (1) shows. The objects 
"words" (shown by W,,) are represented in the 
reclhngles and the objects "sets" (shown by Ci) in 
the parallelograms. 
I ,I 
Fig l : G , ffle ~ mxtd of grc dktkrmy 
_ Lecture 1 coat.dr N) 
f 
~¢" b~tcau de pEda~ d¢ ~" 
/,/pla~tnc,~ J 
= , , 
Leetare t =ppartmh" it, ~t Inclns dtas 
Fig 2" ~npb ofa ~ 
2.1 ) MOVI~IG H~M Ti~ l.E~ To'n~RIOn" 
When moving through Dicologique from the left to 
the right, we move from the general to the specific. 
a) Ikfalifions Imlast ta tbe nlnlim fmm llle Idt to the 
* Suplx~ we talc ~ a set aaatah~ in C, 
"the ~ r,(c~ C9 reinsures the dep6 o~ ~ 
indusk~ 
F~amp~ : 
P(bate~ 1, ~delCx:he 2) = 1 
~tmm) =0 
1 lua~:lx~t 2~depa~: tisl~l~t 
ACRES DE COLING-92, NAr¢1 .y.s, 23-28 AOt~rr 1992 9 8 3 Pgoc. OF COLING-92, NANTES, AUG. 23-28, 1992 
* Set G c~ mr~n 1 toq~ setsQ 0 goes from 1 ~qJ, 
~ is ~ nun~x of sd~s dffld~ of CO. 
* Suppose we take C(G) ~ set omm~ aU ~ ~:ct 
sub~ or childrm, oft, 
c(c0 = {q, ~ th~ O<=j<=V and l~Ci, q) 
=1} 
C0mm) = {fomm,~#c~), ~deki~)} 
* A set C~ ccrmfins 1 to Y termiml ~:mts Wu (u goes 
from 1 ~o Y, Y is the ~ of tmninal ~ds chiktren 
oft0. 
* ~ we take A(Ci) the set of ~ ~x:fly 
chXtmn of C~ 
A(C0 = {W.mchth~ 1 <=u<=Y} 
~am#: 
A~au) = {batea~ rav~, ~} 
In o.~ exarr~, ~ A pmduees tbe syrmyrm for 
boa~ Becmm of their po~im on ~ gta# we win aim 
cmsid~them as lexical ~ 
* Suppose v~ take a fmc~n M(C0. M(G) oa~s the 
~ofmmtsin a set C, (M(C0 = to) 
* Suplxm we take U(CO, the otrmm of the lexical field 
of G, i.e~ fl,~e set of~rds ~-aah~ in G. 
U(CO = {W~, wilh 1 <= u <= ¢o mxl such that it 
exima set Q, ommmg wu ard m:h ff~t P(G, C9 > = 
o)} 
OCtal) = {bam~, na~ ~ ttm~, 
batmierL ~, ~ (day boat), 
U(ba~ t~ de paso), U(ba~ de #d,e de 
li~r,~)"}. 
This ~ allows to edit, with thor muca~ c, 
without, 1444 veem cumm~ ~mined in ~ set 
"emir" (to dm~to a~r). 
Cann~ : acoxding to our ~ ~ funaim 
U(ki~ 2) ~uld ixov~ a result qui~ diffem~ fium ~ 
actml ¢~akmy Dialog~. ~n fact "~isum" is a 
stmame with se~ml thamnds of words ard se~nal 
k~.ls of ~ sets we lme mt s'~wn k~ figa~e 
(2). 
b) l~peay d file ~ph dwi~t fmm fl~ese 
The existance of the function M(Ci) for all sets Ci 
infers that the sUucUnes of inclusion are without 
31m~u d~ kiir: llmU=lx~t 
5~: olh~r syrmym~bmt 
7hlinix:,~ktx~t 
8dmlt~: ttawkr 
1~ ~r d*t,~h~: ~m~ fa~ boa 
U ~,~ r~be,ie p~im~.tlm~ bm ~ f~i~g 
12h~r: ieisl~ 
loops, i.e. there are sets which are not contah~ in 
any odm, r set but the set of the graph G (root node) 
itself. 
sas on/y o~d ~ ~ ~ ofa~ ~-a# G are 
¢) Semai~ an0 ~mati~ ~ ~ the 
setstmd words 
In ~the~ me 9 types of sets : 
- fot~ types of sets mrmd "lists". 
They give the quasi synonyms, i.e. lists 
of words which are equivalent in ~ and 
identical in grammar. We have the following types 
of grammatical sets : noun, verb, adjective, 
adverb. 
- the set mn-~ "dass" 
A set of this type ¢xmtains nouns which can be 
subsumed under the same concept. 
In our example in figure (2), "bateau" is a set 
containing on the one hand words which represent 
its ~ vahe.s ("bateau', "navire", 
"embarcalion") and ca the ocher "~" of 
specific boats~ 
- the set mn~ "mlazd ~xts" 
~y, the contents and utilizations of this type 
of set are rather various. We need it, for example, 
to ~t the link between "baleinier" and 
"baleine "13, which is not shown in figure 2 so as 
not to weigh down the graph. 
-tbe set mrmd "tlxrne" 
This set contains all the concepts and words 
associated in a particular semantic field. It may also 
contain ~ sets such as "related words" or 
smaller "thetis". 
- the set mmed "des:ripfm" 
It contains the constituents organically ~ to 
a ~ It is only used when absolutely 
necessary for a definition. 
- the set nmmd"~" 
It mabsumes words having the same outstanding 
feature. For example, our set of class "bateau 16ger 
de #che" could be found under a set characterized 
by the feature "small" which differenciates this 
class from other classes of boats. 
As for the words, we have provided them with the 
usual characteristics, i.e. their morphological 
classes (grammar) and their usage labels 
(colloquial, archaic, literary ...) which contain the 
labekine: ~le 
ACRES DE COLING-92. NANTES. 23-28 nofrr 1992 9 8 4 PRoc. OF COL1NG-92, NANTES. AUG. 23-28. 1992 
usual information associated to each word in every 
~ficfionary. 
~ Use ~e p~m d~ia~s ~ t~o~k~e 
Moving through the dictionary from the general to 
the particular is a process widely put into practice 
by users who may either search a precise term to 
be discovered by intersection of associated concepts 
or intend to edit a lexical field or a classified list. 
~ ofana~_ 
The logic of the sets takes into account the logical 
"and", "or" and negation. Here are some examples 
of ~arches which are always based on the 
intersection of sets edited by the function U(Cj) : 
- Search ofapnxfise mm 
Search of the name of the "coiffure du Pape t4''. 
The intersection of "Pape l-s" (theme of 162 words) 
and "chapeau ~6" (list of 180 words) or 
"couronne m (theme of 33 words) produces the 
Wol~ds "\[Jal~ 18", "calott~ 19" et~. in 10 seconds on 
the micro eompuler. 
- Search of v~ds *o extmms aa klea 
Search of the verb to express the idea of "faire 
to~0. and "couper 21". T~ intersection of the 
two lists of corresponding verbs converges on 
alxx~ 20 words (abattre, d6~apiter, ~ter, 
~brancher .. 3. 
- Sea~ of syrmyrm acemting ta a ctm~xt 
The synonym of "voler 2v' such that the meaning is 
more suitable for a bee. The words "bufil'ler 23" and 
"voltige~ '24" ale immediately produced. 
Fd~n ot/m/za/~rds 
It laimpally has two ~ms ." 
- to search among veay wide lists for the tram which 
help ~ get al~xise kiea 
For example, the list of verbs "penser2S" contains 
about .500 ~ verbs that allow to move 
confinously through the whole field concerned. 
16dlapellu : hilt 
| 7oflu\[otl~: oto~l 
~9calme:~ 
2°fake k:mher: to knoak o~r 
21 cx~tll~: IDcut 
~:~g~qxJm 
25!rmmr: t)tiak 
- 1o ha~ a pnxise klea alx~ a ~ 
This is especially interesting for the sets containing 
predetermined taxonomies. 
"lhe edition of the set of class "animals" presents 
the scientific taxonomy of the animal world. Atxmt 
4100 indexed animals can be visualized in a 
~ucture of 500 classes. 
2.2 ~HC}MT~ TOT~L~T 
This is the opposite of the previous wore It 
corresponds to moving from the particular to the 
general. 
a) l~alflims lldad~l to lhe ~ f~m file l'~t ~o 
fl~e left 
A set named Cj may beincluded in 1 to f~ sets C~ (i 
going from 1 to ~, f~ being the number of 
"parents" (main sets) of Cj). 
This function permits, therefore, to move upward 
(towards the root node) in the structure of the sets. 
In figure (2), the set "bateau de l~he de plaisance" 
is defined by the set of sets h = {{bateau de 
p~zhe}, {bateau de phisan~}}. 
A terminal word W. can belong to 1 to I sets Ci (i 
going from 1 to I, I being the number of "parents" 
(main sets) of W,). 
In Dicologique the direct questioning of a word 
gives, as in all dictionaries, the "(quasi)-definition" 
oftbe word. 
tm-aW~ 
"tm~ de ptkt~e" and the set of linl~,~ds "lxdeim". 
b) Pwpmim ame G gra~ we affmed t~s 
It exists for every non primitive object of G, 1 to E 
series of connections which link it to one of these 
primitives. 
Allsmesof~franobjecttal~ 
tog~a" amCia~ ~ M~anee H of ttis objeet. 
c) ~e of me previom d~m~s h~ nku~ique 
The table (3) represents the result of a IXn't of the 
search of the polysemotts word "abattm". We have 
limited the reproduction of the result to the 
polysemous zone only. The left cohimn shows the 
sets containing "abattxe" directly. The colum in 
the middle, the type of set concerned. The fight 
column shows the number of eleraents in the set 
conoerned. 
T~s as~t ~s ~ by t~,xmuer mrtaa~. 
ACI'ES DE COLING-92, \]qANrFS, 23-28 AO~" 1992 9 8 5 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 
IX~m of the set Type ofC M(C0 
Co.ztx~ 6 List (L) 81 
Cot~er tm afl~ 7 L 13 
~s L 262 
Fake lon'tm ~ L 52 
~9 L 6 
Fahe ~,~ ° L 52 
Fo# 0 Camct 23 
l~aiP 1 e~t wa~ 24 
L 81 
~te ~l-d~ ~0 L 52 
Tued 2 L 56 
Avi~ 33 Ret Woals 18 
FaJm ton'/x~ L 52 
L 262 
~: !~tan~ du ra0t "abattre" 
In the first place the elements of the above table 
lead to the following searches which correslx~ to 
moving from the left to the right: 
* Sea~ of ~ of"~me" wilh the mear~ ~ 
The edition of the ~ set "d~mtire" (L) 
produces the 262 verbs which constitute the set 
"d~tnm" in alphabetical order. 
* Smreh of ~ of"abme" wilh lhe mearmg of 
"a,4~ am'f~e ~-a,e¢. 
We apply the logical function "AND" to these sets 
of verbs and about 20 verbs are prtxtueed. 7 to 8 
verbs will be left if we add the list "tuer" as a 
supplementary constraint. The processing lakes 4 to 
5 seconds. 
M~ r,m~ aema~.a~ a, ~ ~,ra~ it is rxmt~ to 
enla~e the idea of"danm" to ~he sets whida irdtde it 
The struetut~ edition of 1400 words contained in 
the "changer" list takes less lhan a minute on the 
micro eomputer. 
The motion from the partieular to the general offers 
much fewer functions than the inveg~e motion. At 
the very most it is used to locate. Often the 
26C¢!1p~: tO Ctlt 
27OoUl~Unathre: l~ctlt down all~e 2ad~i~: t~d~.y 
291:Xtld~fll" : It, lily dot~ 3oht :ar,~g 
311:d~ii: beea 
321mr: tokil 
33aviflll: ~ 
consultation of Dicologique is motivated by the 
search of synonyms. The edition of the contents of 
the terminal nodes of figure 3 appears to be largely 
enough for human users. 
Lectllr e : ¢ont~ 
>62500 
( " 
Lecture : apptrtealr 6, esl:laclmt dta$ 
F~e 4 : apart of~e an~O.t~l eovh~rtnt of 
But the position of the computer is very different : 
if humans possess the structures necessary for 
interpreting the terms, i. e. the linguistic heritage 
and the knowledge of the world, the computer for 
its part possesses neither of them. This is why we 
try to supply it with the lexical knowledge 
absolutely necessary in a coherent system. 
Obviously this knowledge is situated in the 
inheritance from the "primitives" to the words. 
Let's see an example in which the motion from the 
right to the left is applied to a problem of automatic 
indexing in information retrieval. 
2.3 An an~a6m to ~Jt~m~i~ rnrlt'val 
We want the system to retrieve the lexieal key 
elements of the following small piece of text : 
"The accident, on Friday, took place in foggy 
weather. The two ears that cmsbed into each other 
caused a pile up of about 50 vehicles on the 
congested national dual carriageway." 
Strategy for resolution : 
The strictly lexical analysis of this short passage 
win be based on the calculation of the surface 
cormslxmding to each cxmeept (set) covering one or 
more words of this document : the more abstract a 
concept, the bigger its an'face. 
For e~ setq we ~ aerrea ~ earar'~ M(Q) 
and i~ a~ in com0adsm wi~ ~ ~fimia've r~ci, q3, 
with Ci as a iximiti~ wd considering a specifie tms~ 
f~a ~ gen~ ~o ~utar. 
supple v,e ~e Max r,(ci, cj) the nmimma ck~ of 
the graph wha~ver j might be. We know, flora 
experm~ that a~is rmximma a~a is attair~ in 
detafl~ entanerafiom of ~lrds whieh have very pmcise 
n',mings and de,igr~,xm~ thin~ 
ACRES DE COLING-92, NANTES. 23-28 AOt)r 1992 9 8 6 PROC. OV COLING-92. NANTES. AUG. 23-28, 1992 
We define the ftmaim (Cj) to nxasme the dimme 
Izt~ma the om0~ Q and the ~ tl~ ~ 
1,(~. 
Tre anface of a omcept is given by the fmnuh as 
fdkz~: 
S(C4D = A(C~ * M(CO 
wt-~ h specifies ll-le s~ies of ~'meetiom h for the 
cahllat~ of~ mrf~e of C~ 
Nqes: 
1) Each ~amCel~ possesses h evaluations of its 
surface accounting for its h series of connections. 
2) In reality A(Cj) takes into account a 
complementary pieoe of information : the semantic 
characteristics of sets. A set of the type "list" 
introduces a more stretched arc than a set of the 
types "theme" or "related words". 
3) If Dioologique is a general dictionary capable of 
resolving problems of information retrieval 
referring to general language, it is very easy to 
adjust it to a precise problem (for example, the 
thesaurus of a Slxcific undertaking). One only has 
to stretch t~da arc situated on the passage of the 
series of conneclions of each term of the thesaurus. 
The surfaces S, which are situated in a norm 
mferenee, describe a map of meaning on which all 
the continuous calculations of Euclidian geon~try 
are made possible. 
To resolve our problem, it is possible to keep the 
mathematical expressions very simple : a simple 
Each word of the document is recognized in the 
dictionary as it activates all the sects containing it 
according to their specific weights S(Q) which 
depend on their series of ¢x~r~xfions. 
Finally, each set will have been activated k times. 
analysis will ~ into aocount as the most 
relevant set the one which presents the smallest 
ra~o of S(Q)& 
s(cg& treasu~ ~e wdght of~ cma~t in ~e texL 
Our ~ ~a~x~ ~ t~h-~ng of a hm~ in 
~ wi~ the concela~ sm wh~ ~ ~e 
text givm in ~ esan'~e : 
1 ° : car ; 2 ° : a~:lent ; 3 ° :road. 
The olher sets have negligible weights. 
The complete analysis (but useless) takes 5 mhmtes 
on a compatible ~. 
We tree ~ wilh the ~ of the ~malts of our 
~'k on lhe ~'an6c smmtae of the lexi~ We think it 
might be ~ ifwe add a ~ ofthe t~txxt 
v,e use f~r ~ the map of mm~ ~e Ime 
3) MEntoD OF S~ AI~ Eg~fl~ 
It is difficult to present completely the method of 
we use as we lack of the supplementary 
page this would involve. To put it simply, all of the 
100000 words and ctaxent phrases, the 15000 
typified sets have been created manually and are 
continually worked over again, thanks to a 
structuring tool specially developed for this 
purpose. 
The 350 000 ob~,waliom of direct connections of 
words, the 4 000 000 series of inh~tanee which 
we run currently are, proportional to our efforts, 
daily and of better quality. 
The point at issue in this iterative procedure is, of 
course, to ~ appear in Dicologique tendencies 
towards sets of "primitives" in the linguistic sense. 
The number and articulation of these primitives 
should be both sufficient and necessary to define 
the objects depending on them. 
Our will is to continue working on the ~ of 
the lexicon, as to the quantity of vocabulary 
represented of course, but principally with 
to the quality of the ~ of the lexicon. 
Moreover, we have been developing basic 
such as the Semiographe for information retrieval 
which allow us to evaluate the progression of the 
quality of interpretations we olXain. If we hope to 
find partners during COLING 92 who want to join 
us in onr current research projects, we are also 
very interested in meeting people from foreign 
universities and firms who would like to launch a 
version of Dicologique in another language. 
Romix, P (1897) "~ des id6es 
ixa'les rims", Anmrd Cx/ilL 
~tin, E t1990) "L'e,,~om~ ~tuene ~s~e ~x,r 
~ e lexioogr~ U~ Noca de 
P, aam, F 0987) "Senma~ L~pt~e", P~ 
P.U.F. 
l)ta~ D 0991) "Un ~ dietiom'a~ de la 
~,ue ~", Lat~ve ~ m~ OLF. 
Heft, P (1991) "Le~ ~ dectnmk\]ues : 
~nmtR,~ deslang~ v~ 
AcrEs DE COLING-92, NAme.s, 23-28 AO~r 1992 9 8 7 PROC. OF COLING-92. NANTES, Aut3.23-28, 1992 
