LEXICON-GRAMMAR AND THE SYNTACTIC ANALYSIS OF FRENCH 
Maurice Gross 
Laboretoire d'Automatique Documentsire et Linguistique 
University of Paris 7 
2 place Jussieu 
75251 Paris CEDEX 05 
France 
ABSTRACT 
A lexicon-grammar is constituted ot the elementary sentences of 
a language. Instead of considering words as basic syntactic units 
to which grammatical information is attached, we use simple 
sentences (subject-verb-objects) as dictionary entries, Hence, s 
full dictionary item is a simple sentence with a description of the 
corresponding distributional and transformational properties, 
The systematic study of French has led to an organization of 
its lexicon-grammar based on three main components: 
- the lexicon-grammar of free sentences, that is, of sentences whose 
verb imposes selactionel restrictions on its subject and complements 
(e.g. to fall, to eat, to watch), 
- the lexicon-grammar of frozen or idiomatic expressions (e.g. 
N takes N into account, N faiaea a question, 
- the lexicon-grammar ot support verbs. These verbs do not have the 
common selactional restrictions, but more complex dependencies 
between subject and complement (e.g. to have, to make in 
N has an impact on N, N makes a certain impression on N) 
These three components interact in specific ways. We present 
the structure of the lexicon-grammar built for French and we discuss 
its algorithmic implications for parsing. 
The construction of a lexicon-grammar of French has led to an 
accumulation of linguistic information that should significantly 
bear on the procedures ot automatic analysis of natural languages. 
We shall present the structure of a lexicon-grammar built for French 
<2> and will discuss its algorithmic main implications. 
1. VERBS 
The syntactic properties of French verbs have been limited in 
terms of the size of sentences, that is, by restricting the type of 
complements to object complements. We considered 3 main types of 
objects: direct, and with prepositions ~ and de. Verbs have 
been selected from current dictionaries according to the 
reproducibility of the syntactic judgments carried out on them by a 
team of linguists. A set of about 10~000 verbs has thus been 
studied. 
The properties systematically studied for each verb are the 
standard ones: 
1 E.R.A. 247 of the C.N.R.S. afiliated to the Universities Paris 
7 and Paris Viii. 
2 Publication of the lexicon-grammar is under way. The main 
segments available are: Boons, Guillet, Lecldre 1976a, 1976b and 
Gross 1975 for French verbs, Giry-Schneider 1978, A. Meunier 1981, 
de Ndgroni 1978, for nominalizations, 
- distributional properties, such as human or non human nouns, and 
their pronominal shapes (definite, relative, interrogative pronouns 
<3>, clitics), possibility of sentential subjects and complements 
que   (that S), ai 3 (whether S, if S) or reduced infinitive 
forms noted V Comp, 
transformational properties, such as passive, extraposition, 
clit icization, etc, 
/~logether, 500 properties have been checked against the 1~000 
verbs <4>. 
More precisely, each property can be viewed as a sentence form. 
Consider for example the transitive structure 
(1) N O V N 1 
We are using Z.S. Harris' notation for sentence structure: noun 
phrases are indexed by numerical subscripts, starting with the 
subject indexed by 0. We can note the property "human subject" in 
the following equivalent ways: 
(2) Nhum V N 1 or N O (:: Nhum) V N t 
w~ere the symbol :: is used to specify a structure . A passive 
structure will be noted 
(3) N I be V-ed by N O 
A transformation is a relation between two structures noted "=°': 
(1) = (3) corresponds to the Passive rule 
The syntactic information attached to simple sentences can thus be 
represented in a uniform way by means ot binary matrix (Table 1). 
Each row ot the matrix corresponds to a verb, each column to a 
sentence form. When a verb enters into a sentence form, a "+" sign 
is placed at the intersection of the corresponding row and column, 
if not s "-" sagn. The description of the French verbs does not 
have the shape of a 10,000x500 matrix. Because of its redundancy 
(cf. note 4 1, the matrix has been broken down into about 50 
submatrices whose size is 200x40 on the average. It is such a 
system of submatrices that we call a lexicon-grammar. 
J Actually, the shape of interrogative pronouns: qu~ (who), 
que-quoi (what) has been used to define a formal notion of 
object. 
4 Not all properties are relevant to each of the 10~000 verbs. 
For example, the properties of clitics associated to object 
complements are irrelevant to intransitive verbs. 
275 
i!tt I dt~-, 
='eml:~l,¢ 
- - - ='em~ 
4- -+ + 4- 4. - ;'e~lmmmr 
+-4.4-+ b~km 
- + ÷ - -- :=pkmr 
+ - - - m f~ 
+ .... 
+-4"4- 
4---- 
+-+- 
+- -- 
+-+- 
+-++ 
@--- 
+--- 
N I 
:tz i z I =" Iz I z I z I =. }; 
-4"- ---+---+- 
..... +- - +! 
4= + .... +-+--i 
4= ++-@-+-4"-- 
o==u~ ++-+-++ 
4"+--+-++ 
--4-- 
e= - -+---4-I 
de - -÷-4---i 
4= -÷---+-+-- 
.I > I~. 
z:lz~ 
-ii + ÷ 
÷ 
+ 
- -\]rm~ + + - + - + - 4. .... ,~= 
-ear~ 
2-+;;-- .++'-*- + ; - 4.-@- 
-l|uW dkw==¢ + .... + + 
Intransitive Verbs (From Boons. GuilipP. r~l "~ S, Guillet, 5ecl~re 1976a) 
Table 1 
Although the 3 prepositions "zero", a and de ere felt and 
described as the basic ones by traditional grammarians, the 
descriptions have never received any objective bee,s. The 
lexicon-grammar we have constructed provides s general picture of 
the shapes of obleCts tn French. The numerical distr,butlon of 
oblect patterns is given ,n table 2, according to their number in a 
sentence and to their preposlhonal shape. 
N O V 
N O V N 1 
NoV&N 1 
N O V de N I 
N O V N 1 N 2 
N O V N 1 ~= N 2 
N O V N 1 de N 2 
NoV&N1 &N 2 
N O V & N 1 de N 2 
N O V de N 1 de N 2 
!,800 
3,700 
350 
500 
150 
1,600 
1,900 
3 
10 
1 
DISTRIBUTION OF OBJECTS 
Table 2 
AS can be seen on table 2, direct oblects are the most numerous in 
the JPXlCOn. Also, we have not observed a single example of verbs 
with 30blects according to our definition. 
In 2. and 3. we will make more precise the lexicel nature of 
the Nl's attached to the verbs. 
The signs in a row of the matrix provides the syntactic 
paradigm of a verb, that is, the sentence forms into which the verb 
may enter. The lexicon-grammar is in computer form. Thus, by 
sorting the rows of signs, one can construct equivalence classes for 
verbs: Two verbs are in the same class if their two rows of signs 
are identical. 
We have obtained the following result: for 10,000 verbs there 
are about 8,000 classes. 
On the average, each class contains 1.25 verb. This 
statistical result can easily be strengthened. When one studies the 
classes that contain more than one verb, it is always possible to 
find syntactic properties not yet in the matrix and that will 
separate the verbs. Hence, it our description were extended, each 
verb would have • unique syntactic paradigm. 
Thus, the correspondence between a verb morpheme end the set of 
sentence forms where it may occur is one-to-one. 
Another way of stating this result is by saying that structures 
depend on individual lexical elements, which leads to the following 
representation of structures: 
N O eat N 1 
N o owe N 1 to N 2 
We still use class symbols to describe noun phrases, but specific 
verbs must appear in each structure. Class symbols of verbs are no 
longer used, since they cannot determine the syntactic behsviour of 
individual verbs. 
The nature of the lexicon-grammar should then become clearer. An 
entry of the lexicon-grammar of verbs is • simple sentence form with 
an explicit verb appearing in • row. In general, the decleretive 
sentence is taken as the representative element of the equivalence 
class of structures corresponding to the "+" signs of a row. 
The lexicon-grammar suggests a new component for parsing 
algorithms. This component is limited to elementary sentences. It 
includes the following steps: 
- (A) Verbs are morphologically recognized in the input string. 
- (B) The dictionary is looked up, that is, the space of the 
lexicon-grammar that contains the verbs is searched for the input 
verbs. 
- (C) A verb being located in the matrix, its rows of signs provide 
a set of sentence forms. These dictionary forms are matched with 
the input string. 
This algorithm is mcomplete in several respects: 
- In step (C). matching one of the dictionary shapes with the input 
string may involve another component of the grammar. The structures 
represented in the lexicon-grammar are elementary structures, 
subject only to "unary" transformations, in the sense of Harris' 
transformations or of early generative grammar (Chomsky 1955). 
Binary or generalized transformations apply to elementary sentences 
and may change their appearance in the sentence under analysis (e.g. 
conjunction reduction). As a consequence, their effect may have to 
be taken into account in the matching process. 
276 
Looking up the matrix dictionary may result in the finding of 
several entries with same form (homographs) or of several uses of a 
given entry. We will see that these situations are quite common. 
in general, more than one pattern may match the input, mulbple 
paths of analysis are thus generated and require book keeping. 
We will come back to these aspects of syntactic computation. 
We now present two other components of the lexicon-grammar of simple 
sentences. 
2 IDIOMS 
The sentences we just described can be called free sentences, 
for the lexlcal choices Of nouns in each noun phrase N i has 
certain degrees of freedom. We use this distributional feature to 
separate free from frozen sentences, that is, from sentences with an 
idiomatic part. 
The main difference between free end frozen sentences can be 
stated in terms of the distributions of nouns: 
- in a frozen nominal posibon, a change of noun either changes the 
meaning of the expression to an unrelated expression as in 
to lay down one's arms vs to lay down one's feet 
or else, the variant noun does not introduce any difference in 
meaning (up to stylistic differences), as m 
to put someone off the (scent. track, trail) 
or else. an idiomatic noun appears at the same level as ordinary 
nouns of the distribution, and the general meaning of the (free) 
expression is preserved, as in 
to miss (an opportunity, the bus\] 
- in a free position, a change of noun introduces a change of 
meaning that does not affect the general meaning of the whole 
sentence. For example, the two sentences 
The boy ate the apple 
My sister ate the pie 
that differ by distributional changes in subject and object 
positions have same general meaning: changes can be considered to 
be localized to the arguments of the predicate or function with 
constant meaning EAT. 
We have systematically described the idiomatic sentences of 
French, making use of the framework developed for the free 
sentences. Sentential idioms have been classified according to the 
nature (frozen or not) of their arguments (subject and complements). 
With respect to the structures of Table 2, a new classificatory 
feature has been introduced: the poaslbdity for a frozen noun or 
noun phrase to accept a free noun complement. Thus, for example, we 
built two classes CP1 and CPN corresponding to the two types of 
constructions 
N O V Prep C 1 :: Jo plays on words 
N O V Prep Nhum'a C 1 =: Jo got on Sob's nerves 
The symbol C refers to a frozen nominal position and Prep 
stands for preposition. 
Although frozen structures tend to undergo less transformations 
than the free forms, we found that every transformation that applies 
to a free structure also applies to some frozen structures. There 
is no qualitative difference between free and frozen structures from 
the syntactic point of view. As a consequence, we can use the same 
type of representation: a matrix where each idiomatic combination 
of words appears in a row and each sentence shape m a column (of. 
Tables 3 and 4), 
I SiJJElS 
=m 
T',' 
+ - 
+ - 
+ - 
+ - 
+ - 
÷ - 
+ - 
+ - 
+ + 
+ - 
+ - 
¢ - 
+ - 
+ - 
+ - 
. _ 
+ - 
¢ , 
÷ - 
÷ - 
V(RB($ ADVEnES rIG(S 
VENIR DAMS 
PARTIR 5UR 
DEMONTRER N A N PAR 
PARTIR DANS 
DIRE NAN ~N 
TRICHER 
ARRETER.$-. 
VENIR A 
ESPERER N DE 
ARRANGER N A 
OAGNER N A 
VENIR CONTRE 
PARTIR A 
VENIR PAR 
PATER N A 
CONSULTER N A 
CONSULTER N DANS 
CHOISIR N A 
DISCUTER 
BOIRE N AVANT 
SPECULER A 
PARLER 
TRICHZR DE 
FONCER A 
AGIR A 
CUIRE N A 
FONCER A 
CUIRE N A 
ACCEPTER N EN 
RIRE DE 
LUTTER JUSOU'A 
CUIRE N $UR 
FONCES A 
CUIP~ N A 
VENIR PAR 
CUIRE N A 
CUIRE N A 
DORNIR ~N 
CUIRE N SOUS 
REMBOURSER N A 
LA "PERIODE" 
CE 
L' ABSURDE 
L' AFFIRMATIVE 
L' AIR 
POSS-O AISE 
L' ALLER 
TOUTS ALLURE 
TOUTE POSS-O 
L' AMIABLE 
L' ARRACHE 
TOUTE ATTENTE 
L' AUBE 
L' AUTOSUS 
L' AVANCE 
L' AVENIR 
L' AVBNIR 
L' AVEUGLETTE 
TOUT AZIP~T 
LA BAGARP~ 
LA BAISSE 
TOUT RAS 
PLUS BELLE 
TOUTS BERZINGUE 
LE BESOIN 
LE BEURRE 
TOUTS BITURE 
LE B015 
TOUTE BONNE FOI 
TOUTS POSS-O BOUCHE 
LE BOUT 
LA BRAISE 
TOUTS BRIDE 
LA BROCHE 
LE BUS 
LE BUTAGAZ 
LE BUTANE 
TOUT CAS 
LA CENDNE 
LE CENTUPLE 
Frozen adverbs 
Table 3 
We have systematically classified I15.000 idiomatic sentences, When 
one compares thls figure with those of table 2', one must conclude 
that frozen sentences constitute one of the most important 
components of the lexicon-grammar. 
An important lexlcal feature of frozen sentences should be 
stressed. There are examples such as 
They went astray • 
where words such as astray cannot be found io any other 
syntactically unrelated sentence; notice that the causative sentence 
The# led them astray 
Is considered as syntactically related. In this case, the 
expression can be direcly recogmzed by dictionary look-up. But 
such examples are rare. In general, a frozen expression is • 
compound of words that are also used in free expressmns wJth 
unrelated meanings. Hence, frozen sentences are in general 
ambiguous, having an ~dmmahc meaning and a literal meaning. 
277 
However, the hteral meanings are almost always mcongruous In the 
context where the idlomahc meamng is mtended (unless of course 
tr:e author of the utterance played on words). Thus, when a word 
combination that constitutes an idiom is encountered m a text, one 
IS practically ensured that the corresponding meaning is the 
idiomatic one, 
I 
0 ! I 
;;I "I 
o H 
I u 
N 
* • CONNAITRE 
COMNAITRE 
CO~NAZTRE 
NE CONNAITRE PAS 
: NE CONNAITRE OUR 
CONSERVER 
SE CONTEKPLER 
COUPER 
DEBLOQUER 
DETENIR 
DISTILLER 
DOMINER 
DRESSER 
Erfl)OSSER 
ENFONCER 
£TRE . N PAS 
ETRE . N PAS 
ETRE . N FAS 
ETRE . S DIT 
FAIRE 
FAIRE 
FAIRE 
FAZRE 
i FAIRE 
FAIRE 
\] FAIRE 
j FAIRE 
FAIRE 
I FAI~ 
J FAIRE ENTENDRE 
FAIRE PASSER 
FAIRE SAUTER 
FERVOR 
FLETRIR 
FORCER 
FOR~R 
FORMER 
FORMER 
FORNER 
FRANCHIR 
I 
! I 
I | ' \] 
-~ .E .'3 .=~ 
I 
- * L£ COUP 
- - POSS-¢ i i DOULEUR - + L£ TRUC 
- - POSS-~ BONH£UR 
- - - CA 
- - POSS-¢ CHgHISE 
r - - LE NOMBRZL 
- . det SITUATION 
+ - LA VERITE 
LE VENIN 
- + LE LOT 
J- , POSS-(P - ÷ BATTERTES 
J - ~ LE HARNOIS - ~ LE CLOU - . UNE LUHIERE i: NORT 'NC"OT 
ii! Tout N BRIN DE TOILETTE 
GRISE MZN~ 
HARA-KIRI 
JURISPRUDENCE 
;- + UN£ NINUTE DE SILENCE 
NO~BRE 
:- + DET OPERATION PORTE OUVERTE 
- - DU QUARANTE CINO FILLETTE 
TAPIS 
TINTIN 
- - POSS-~ VOIX 
- - DET ENFANT 
- - DET ENFANT 
- * POS$-~ PORTES 
- + DET CRIME 
_ _ LA CHANCE 
- + L£ CARRE 
- ~ DET NUNERO 
- + DET NUNERO DE TELEPHONE 
- . LES PANGS 
i - . DET CAP 
Frozen sentences 
Table 4 
Returmng to the algorithm sketched in 1, we see that we have 
to middy steps (A) and (B) in order to recognize frozen 
expressions: 
- NOt only verbs, but nouns have to be immediately located in the 
input string. 
- The verbs and the nouns columns of the lexicon-grammar of frozen 
expressions have to be looked up for combinations of words. 
It Js mterestmg to note that there is no ground for stating a 
priordy such as look up verbs before nouns or the reverse. Rather, 
the nature of frozen forms suggests simultaneous searches for the 
composing words. 
About the diHerence between free and frozen sentences, we have 
observed that many free sentences (if not all) have highly 
restricted nominal posdlons. Consider for example the entry 
N O smoke N t =n 
Jo smokes the finest tobacco 
In the direct object complement, one will find few other nouns: 
nouns of other smoking material, objects made of smoking material 
such as cigarette, cigar, pipe and brand names for 
these oblects. This is a common situation with technical verbs. 
Such examples suggest that, semantically at least, the nominal 
arguments are halted to one noun, which comes close to having the 
status of frozen expression. Thus, to smoke would have here 
one complement, perhaps tobacco, and all other nouns occurring 
m its place would be brought in by syntactic operations. We 
consider that this situatmn is quite general although not always 
transparent. Our analysis of free elementary sentences has shown 
that when subjects and Oblects allow wide variations for their 
nouns, then well defined syntactic operations account for the 
variation: 
- separation of entries: For example, there is another verb 
N O smoke Nt, as m They smoke meat, and a third one: 
N O smoke N 1 out in They smoked the room out; or 
consider the verb to eat in 
Rust ate both rear wings of my car 
This verb will constitute an entry different of the one in to eat 
lamb; 
various zerolngs: The following sentence pairs will be related 
by different deletions: 
Bob ale s nrce preparation 
= Bob ale a nice preparation of lamb 
Bob ate a whole bakery 
= Bob ate a whole bakery of apple pies 
Other operations introduce nouns in syntactic positions where 
they are foreign to the semantic distributions, among them are 
ralsmg operations, which induce distributional differences such 
as 
I imagined the situation 
I imagined the bridge destroyed 
situation is the "natural" direct oblect of to imagine, 
while brrdge ts derived; 
- other restructuration operations (Gulllet, Lecl~re 1981), as 
between the two sentences 
This confirmed Bib's opinion of Jo 
This confirmed Bob m his opinion of Jo 
Although the full lexicon of French has not yet been analyzed 
from this point of view, we can plausibly assert that a targe class 
of nommal distributions could be made semantically regular by using 
Z.S. Harris' account of elementary distributions, namely, by 
determining a basic form for each meaning, for example 
A person eats food 
with undetermined human subject and characteristic object, and by 
278 
introducing classificatory sentences that describe 
universe: 
(The boy, My sister) ia • person, etc. 
the semantic 
(A pie, This cake) is food, etc. 
Classificatory and basic sentences are combined by syntactic 
operations such as 
relatlvizstion: 
The person who is the boy eats food which is this pie 
WH-ia deletion: 
The person the boy eats food this pie 
redundancy removal: 
The boy eats this pie 
In this way, the semantic variations are explicitly attributed 
to lexical variations, and not to intuitive abstract features, that 
is, arbitrary features, or acmes or the like. The requirement of 
using WORDS in such descriptions is a crucial means for controlling 
the construction of an empirically adequate linguistic system. In 
this respect, one is led to categorizing words by evaluating actual 
classificatory sentences. Hence, all the knowledge linguistically 
expressible (i.e. in terms of words) is represented by both the 
basic and the classificatory sentences. A good deal of the 
inferences that one has to draw in order to understand sentences era 
contained in the derivations that lead to the seemingly simple 
sentences. 
From a formal point of view, the entries of the lexicon-grammar 
become much more specifi~ We have eliminated class symbols 
altogether, replacing them by specific nouns <5>. Entries are then 
of the type 
{persen) 0 eat (food) 1 
(person) 0 ;We (ObleCt) 1 to (person) 2 
(per=ran) 0 k~ck the bucket 
An application of this representation of simple sentences is the 
treatment of certain metaphors. Consider the two sentences 
(1) Jo filled the turkey with truffles 
(2) Jo filled his report with poor jokes 
(1) is a proper use of fo fill, while (2) is • metaphoric or 
figurative meaning. The properties of these sentences vary 
according to the lexical choices in the complements {Boons 1971). 
For example, the with-complement that can be occupied by an 
internal noun in the proper meaning can be omitted: 
Jo tilled the turkey with • certain filling 
= Jo filled the turkey 
5 It is doubtful that actual nouns such as food will be 
available in the language for each distribution of each entry, but 
then, expressions such as smoking stuff can be used {in the 
object of to smoke), again avoiding the use ot abstract 
features. 
iThis is not the case in the figurative meaning: 
*Jo filled hie report 
How to represent (1) and (2) is a problem in terms of number of 
entries. On the one hand, the two constructions have common 
syntactic and semantic features, on the other, they ere 
significantly different in form and content. Setting up two entries 
is • solution, but not a satisfactory one, since both entries are 
left unrelated. A possible solution in the framework of 
lexicon-grammars is to consider having just one entry: 
N O fill N 1 with N 2 
and to specify N t lexJcally by means of columns of the matrix. 
For example 
N 1 =: food 
N t =: text 
11~en, the content of N 2 is largely determined end has to be 
roughly of the type 
N 2 =: stuffing 
N 2 =: eubtext 
An inclusion relation <6> holds between the two complements. We can 
write for this relation 
N 2 is in N 1 
But now, in our parsing procedure, we have to compensate for 
the tact that in the lexicon-grammar, the nouns that are represented 
in the free positions ere not the ones that in general occur in the 
input sentences. In consequence, occurrences of nouns will have to 
undergo a complex process of identification that will determine 
whether they have been introduced by syntactic operations (e.g. 
restructuration), or by chains of substitutions defined by 
classificatory sentences, or by both processes. 
3. SUPPORT AND OPERATOR VERB8 
We have alluded to the tact that only • certain class of 
contences could be reduced to entries of the lexicon-gremmr as 
presented in 1. and 2. We will now give examples of simple 
sentences that have structures different of the structures of free 
and frozen sentences, in sentences such as 
(1) Her remarks made no difference 
(2) Her remarks have some (importance for, influence) on Jo 
(3) Her remarks ere in contradiction with your plan 
it is difficult to argue that the verbs to make, to have 
and to be in semantically select their subjects end 
complement& Rather, these verbs should be considered as 
auxiliaries. The predicative element is here the nominal form in 
complement position. This intuition can be given a formal basis. 
Let us look at nominalizationa as being relations between two simple 
sentences (Z.S. Harris 1964), as in 
6 This relation is an extension of the Vaup relations of 3. 
To fill could be considered as a (causative) Vop. 
279 
Max walked 
: Max look a walk 
Her remarks are important for Jo 
= Her remarks are of a certain importance for Jo 
= Her remarks have s certain importance for Jo 
Jo resembles Max 
: Jo has a certain resemblance with Max 
= Jo (bears. carries) a certain resemblance with Max 
-- There is a certain resemblance between Jo and Max 
It is then clear that the roots walk, important and 
resemble select the other noun phrases. We call support verbs 
(Vsup) the verbs in such sentences that have no selectional 
function, Some support verbs are semantically neutral, others 
introduce modal or aspectual meanings, as for example in 
Bob loves Jo 
= Bob Is in love with Jo 
= Bob fell in love with Jo 
= Bob has a deep love for Jo 
to tall, as other motion verbs do, introduces an inchoative 
meaning. In this example, the mare semantm relation holds between 
Bob and love, and the support verbs simply add their 
meaning to the relation. 
If we use s dependency tree to schematize the relations in 
simple sentences, we can oppose ordinary verbs with one obleCt and 
support verbs of superficially identical structures such as in 
figure 1: 
described 
Ma~x love 
Bo b's~~ Jo 
Two problems arise in connection with the distribution of support 
verbs: 
- s noun or a nommalized verb accepts a certain set of support 
verbs and this set varies with each nominal; 
not every verb is a support verb; thus in the sentence 
(4) Max described Bob'a love for Jo 
to describe is not a Vsup. The question is then to delimit 
the set of Vaups, if such a set can be isolated, or else to 
provide general conditions under which s verb acts as a Vaup, 
One of the structural features that separates support verbs 
from other verbs is the possibility of clefting noun complements. 
For example, for Jo is a noun complement of the same type in 
both structures, but we observe 
*If is for Jo that Max described Bob'a love 
It is for Jo that Bob has a deep love 
The main semantic difference between the two constructions lies in 
the cyclic structure of the graph. This cyclic structure is also 
found in more complex sentences such as 
(5) This note put her remarks in contradiction with your 
plan 
(6) Bob gave a certain importance to her remarks 
Both verbs fo put and to give have two complements, 
exactly as in sentences such as 
(7) Bob put (the book) 1 (in the drawe~| 2 
(8) Bob gave (e book) t (to Jo) 2 
Whde in (7) and (8), there is no evidence of any formal relation 
between both complements, in (5) and (6) we find dependencies 
already observed on support verbs (cf. figure 2). 
gave 
B°~msrks 
has BJ  ove put 
The notre ~ her remarks, in contra~ctmn 
\ 
with your plan 
Figure I Figure 2 
280 
The verbs to put and to give are semantically minimal, for 
they only introduce s causative and/or an agentive argument with 
respect to the sentence with Vsup. We call such verbs operator 
verbs (Vop). There are other operator verbs that add various 
modaltties to the minimal meanings, as in 
The note introduced a contradiction between her remarks 
and your plan 
Bob attributed a certain importance to her remarks 
Other syntactic shapes are lound: 
Bob credsted her remarks with a certain importance 
Again, the set of nouns (supported by o Vsup) to which the 
Vops apply vary from verb to verb. As a consequence, we have to 
represent the distributions of Vsups and Vops with respect to 
nominals by means of a matrix such as the one in Table 4'. 
In each row, we place a noun and each column contains a support verb 
or an operator verb. A preliminary classification of Ns (and 
V-ns) has been made in terms of a few elementary support verbs 
(e.g. to have, to be Prep). 
In a sense, this representation is symmetrical with the 
representation of free sentences. With free sentences, the verb is 
taken as the central item of the sentence. Varying then the nouns 
allowed with the verb does not change fundamentally the meaning of 
the corresponding sentences. With support verbs, the central item 
is a noun. Varying then the support verbs only introduces a 
distributional-like change in meaning. 
The recognition procedure has to be modified, in order to 
account for this component of the language: 
- first, the took-up procedure must determine whether s verb is an 
ordinary verb (i.e. an entry found in a row of the lexicon-grammar) 
or a Vaup or a Vop, which are to be found in columns; 
- simultaneously, nouns have to be looked up in order to cheek 
their combination with support verbs. 
4. CONCLUSION 
We have shown that simple sentence structures were of varied 
types. At the same time, we have seen that their representation in 
terms of the entries of traditional "linear" dictionaries, that is, 
In terms of words alphabetically or otherwise ordered, is 
inadequate. An improvement appears to involve the look-up of 
two-dimensional patterns, for example the matrices we proposed for 
frozen sentences and their generalization to support verbs and 
operator verbs. More generally, syntactic structures are determined 
by combinat|ons of a verb morpheme with one or more noun 
morpheme(s). Hence, the general way to access the lexicon will have 
to be through the selectional matrix of Tables 3 and 4, 
In practice, syntactic computations are context-free 
computations in natural language processing. Context-free 
algorithms have been studied in many respects by computer 
scientists, theoreticians and speciahsts ot programming languages. 
The principles of these algorithms are clearly understood and 
currently in use, even for natural languages where new problems 
arise because of the numerous ambiguities and the various 
terminologies attached to each theoretical viewpoint. 
The tact that context-free recognition is a mastered technique 
has certainly contributed to the shaping of the grammars used in 
automatic parsing. The numerous sample grammars presented so far 
are practically all context-tree. There is also a deep linguistic 
reason for building context-free grammars: natural languages use 
embedding processes and tend to avoid discontinuous structures. 
Much less attention has been peJd to the complex syntactic 
phenomena occurring Jn simple sentences and to the organization of 
the lexicon. The tact that we could not separate the syntactic 
properties of verbs from their lexical features has led us to 
construct a representation for linguistic phenomena which is more 
specJhc than the current context-free models. A context-free 
component will still be useful in the parsing procesS, but it will 
be relevant only to embedded structures found in complex sentences, 
with not much incidence on meaning, 
To summarize, the syntactic patterns are determined by pairs 
(verb, noun): 
- the frozen sentence N O k~ck the bucket Js thus entirely 
specified, while the pair (take, bull) needs to be 
disambiguated by the second complement by the horns, requiring 
thus a more complex device to be identified; 
(take, walk) and (take, food) are support 
sentences, so are (have, faith) and (have, food); 
the verbs have, kick and take together with 
concrete obiect select ordinary sentence forms. 
But the selectional process for structures may not be direct. 
The words in the previously discussed pairs may not appear in the 
input text. Words appearing in the input are then related to the 
words in the selectJonal matrix by: 
cfassifJcatlonal relations: 
food classifies cake, soup, etc. 
concrete obiect classifies ball, chair, etc. 
- relations between support sentences, such as 
Jo (had, took,threw out) some food 
Jo (took, was out for, went out for) a walk 
Jo (has, keeps, looses) faith in Bob 
relations between support and operator sentences: 
Thie gave to Jo faith in Bob 
All these relations in fact add a third dimension to the 
selectional matrix. 
The complete selectional device is now a complex network of 
relations that cross-relates the entries. It will have to be 
organized in order to optimize the speed of parsing algorithms. 
281 
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282 
