NATURAL LANGUAGE INPUT FOR SCENE GENERATION M 
Giovanni Adorni, Mauro Di Manzo 
Istituto di Elettrotecnica, University of Genoa 
Viale F.Causa 13, 16145 Genoa, Italy 
Giacomo Ferrari 
Istituto di Linguistica Computazionale, CNR 
Via della Faggiola, 56100 Pisa, Italy 
ABSTRACT 
In this paper a system which understands 
and conceptualizes scenes descriptions in natural 
language is presented. Specifically, the following 
components of the system are described: the syntac- 
tic analyzer, based on a Procedural Systemic Gram- 
mar, the semantic analyzer relying on the Conceptu- 
al Dependency Theory, and the dictionary. 
I INTRODUCTION 
In this paper a system is presented, which under 
stands and conceptualizes scenes descriptions in 
natural language (Italian) and produces simple stat 
ic images of the scenes. It is part of a larger 
project that aims at understanding the description 
of static scenes, reasoning (in case of incom- 
pleteness or inconsistency) and dialoguing about 
them, and finally generating and displaying them. 
The Input Analyzer (IA) of the system is the 
most stable end experimented component and it is 
the topic of this paper. It consists of a Syntactic 
Analyzer, a Cognitive Data Base (CDB) and a Seman- 
tic Interpreter. 
II SYNTACTIC ANALYZER 
The syntactic analysis is performed by means of 
a Procedural Systemic Grammar (PSG) (McCord,77). 
The main characteristics of the PSG parser is that 
the operation flow is highly structured, since 
different levels of the analysis are associated to 
the syntactic units of the sentence. Five processes 
can be activated (CLAUSE, COMPL.GR, NOUN.GR, ADJ.GR 
and VERB.GR) devoted to recognize respectively: 
(i) the sentences, (ii) the propositional phrases, 
comparatives, quantification and noun phrases, 
(iii) the components of the noun phrases, (iv) the 
adjectives and their modifiers, (v) the verb and 
its modifiers. 
Fig.l shows how these processes can interact in our 
parser: double arrows indicate message passing and 
Work supported by M.P.I. under grant 27430/81 
single 
I CLAUSE \[.- 
,.1 ........ ...... ............ 
h ! v--.o. 
arrows indicate reading from input. Each 
I N°UN'G"~I-'- I 
i ADJ.GR I- I- 
® 
Fig.l - Levels of Syntactic Analysis 
level is activated by the superior one, as shown in 
Fig.l, and returns to its caller the results of 
its computation as a message. A feature network 
is associated to each process, which is activated 
together with its corresponding processes. 
In a PASCAL-like language the feature network can 
be defined as follows: 
type FEATURE .... (.LIST OF FEATURES.)...; 
LINK=^NODE; 
NODE=record 
NAME:FEATURE; 
VALUE:boolean; 
FATHER,NEXT_BROTHER:LINK; 
FIRST_SON,ALTERNAT:LINK; 
end; 
FEATURE NETWORK:array(FEATURE) of LINK; 
Each NODE represents s feature identified by its 
NAME; the ALTERNATE pointer allows the connection 
in a Circular list of mutually exclusive features 
as in SHRLDU (Winograd,72). Each process gives as 
output a fragment of the FEATURE NETWORK manipu- 
lated to describe the input; this is performed by 
means of a set of functions which test the presence 
175 
of a feature in the FEATURE_NETWORK, add and erase 
features, as described in McCord ('77). The process 
is divided into a set of sequential routines,called 
SLOTs, analyzing the functional components of a 
Syntactic Unit. In the function: 
function FILLER(ARGI:PROCESS, 
ARG2:SETOF_FEATURES):boolean; 
ARGI activates the appropriate process to fill the 
caller slot; the second argument of the function 
selects the set of features to which the called 
process must be inizialized. 
This last features-passing mechanism is absent in 
the original PSG; from our experience, we found 
it usefull in all the cases in which a choice in 
a syntactic level is determined by the syperior 
level or by a more larger context. 
Thus, for instance, the set of features character- 
izing a prepositional phrase is determined at the 
corresponding syntactic level by the preposition 
and the features of the nominal phrase; but further 
and not less important selection criteria can be 
imposed by the verb which is found in the upper 
level. The output of a simple analysis is shown 
in Fig.2; it gives an idea of the syntactic repre- 
sentation. 
INPUT: IL M~O GATTO STA MANGIANDO {my ca~ is eating) 
RESULT OF THE ANALYSIS: 
TIPO DICHIARAT.TVA ATTIVA (dec lara~ive active} 
IL MIO GATTO SOGGETTO {suOJe¢~} 
STA MANG IANDO VERB . GR 
FEATURE NETWORK : 
--CLAUSE- ! -PROPOSIZIONE- ! -PRINC- ! -DICHIARATIVA-- 
I -VERB. GR ...... ! -PE"S-- ! -TERZA-- 
! I -NUM --- ! -SINGOLARE-- 
! ! -MOUO-- ! -ESPLICITO- ! - INDICATIVO-- 
I ! -TEMPO- ! -PRESENTE-- 
I ! -ACT--- f -TRANSITIVA-- 
! -FORMA- ! -STARE-- 
! f -GENERICO-- 
! -COMPL. UR ..... ! -PERS-- ! -TERZA-- 
! -GEN--- ! -MASCHILE-- 
-gUM--- ~-SZNGOLARZ-- 
-NOUN.C~-~-.ONE-- 
f -TIPO-I-COMUNE PSRS-- 
FIG.2 - Result of a Sentence Analysis 
The choice of PSG is mainly motivated by the possi- 
bility of parallel computation. A control structure 
allowing the parallel computation is: 
cobegin ... coend; 
It is a single input-output structure, very usefull 
to handle alternative choices for the same computa- 
tional level. In the case of mutually exclusive 
alternatives only one of the "n" processes acti- 
vated by a cobegin control structure can end suc- 
cessfully. In the case of not mutually exclusive 
alternatives, it is still possible to use the 
cobegin control structure , but it is necessary 
to define a strategy for the selection of the most 
suitable alternative when the coend occurs. 
An experimental implementation in terms of para~ 
lel computation has been made on a multiprocessor 
system (Adorni et ai.,'79). Another version of this 
parser has been implemented in PASCAL (DiManzo et 
ai.,'79} and a version in FranzLisp is in progress. 
III STRUCTURE OF THE COGNITIVE DATA BASE 
The organization of knowledge, in this system, 
is based on a set of THOUGHTs. A THOUGHT is a frame 
like structure within which new data are interpret- 
ed in terms of concepts acquired through previous 
experience (Minsky,'75), (Schank,Abelson,'77). 
Every THOUGHT has a TYPE which determines a set 
of operations applicable to it. The following 
predefined types are allowed (Adorni,DiManzo,'83): 
- DESCRIPTIVE, that defines the complete descrip- 
tion of a physical,abstract,animate or not,object. 
- PROTOTYPE, that defines the structural part of 
a physical object in terms of generalized cones 
(Marr,Nishihara,'78). An example of definition of 
simple prototype object is given in Fig.3. 
- JOINT, that defines the element of connection 
between physical objects, in order to build more 
complex objects or scenes (Fig.4). 
- SPATIALREL, that defines spatial relationships 
like "on,near,on the left of,..." between objects. 
All the linguistic relationships like "above,under, 
behind", and so on, are reduced into quantitative 
geometrical relationships between the coordinates 
of some points of the involved objects; this choice 
is motivated by the possibility of deriving a set 
of very general inference rules from analytic geom- 
etry (Adorni et ai.,'82), (Boggess,'79), (Boggess, 
Waltz,'79). The coordinates of an indefinite point 
P are given in the form: 
COORD K OF P (REFERRED_TO A)=H 
where K is a group of possible coordinates, H a 
set of values for these coordinates and A is the 
THOUGHT of the object to which the reference system 
used is connected. Fig.5 shows the THOUGHT for an 
use of the preposition "on". 
A spatialrel type THOUGHT can contain conceptu- 
alizations and prototype THOUGHTs; a joint type 
can contain only its description; a prototype type 
can contain joint or prototype THOUGHTs or descrip- 
tions in terms of generalyzed cones;all these types 
can be enclosed in a descriptive type which can 
contain conceptualizations and all the types of 
THOUGHTs, previously introduced. A descriptive type 
can include the following fields (Adorni,DiManzo, 
'83), (see Fig.6): 
- DESCR, contains all the basic not specialized 
knowledge about the object; 
- LEVELS, contains a description of the shape of 
the object (in terms of prototype THOUGHTs) 
divided in different levels of detail hier- 
archically organized; 
- USE, contains the descriptions of the most common 
activities involving the use of the object, 
in terms of spatialrel between prototype 
THOUGHTs; 
176 
FIG.3 - Example of Definition of a Simple Prototype 
FIG.4 - Definition of a Simple Jointing Element and Use of this Element 
to build a More Complex Object 
177 
- POSITION, gives the most common spatial relations 
between the described object and other ob- 
jects in standard scenes, in terms of a spa- 
tialrel between prototype THOUGHTs; 
- SUPPORT, contains the indication, in terms of 
descriptive THOUGHTs, of the objects which 
are supported in standard situations; 
- COLOR and MADE, describe the possible set of col- 
ors and materials, while WEIGHT contains 
information about the range of possible 
weights; 
- CONTENT, says, in terms of descriptive THOUGHTs, 
that the normal use of the object is a con- 
tainer for other objects; 
- DYNAMIC, contains the current expectations about 
the boundaries of the dimensions of the ob- 
jects; it can be dinamically updated every 
time a new object of the same class enters 
the system's CDB. 
IV SEMANTIC INTERPRETER 
The Semantic Interpreter of the IA interacts 
with the Syntactic Analyzer and operates on a set 
of rules in order to build the concepts a sentence 
was intended to mean. The output of this module 
is a Conceptual Dependency Network (Schank,'75), 
in which every nominal is substituted by a complex 
descriptive THOUGHT instantiated from the CDB. 
Let us illustrate the procedure of analysis con- 
sidering the following sentence (the translation 
is word by word in order to reproduce the problems 
of Italian): 
(i) "l'uomo dai capelli grigi e' andato a Roma 
con l'auto di Giuseppe" 
(the man with the grey hair has gone to 
Rome with the car of Joseph) 
The procedure of analysis has several steps: 
A. Analysis of Words and Simple Phrases 
During this step the entities which take part 
into the conceptualization are identified. In fact 
an indexed identifier Xi is associated to each ob- 
ject referred to in the sentence (each nominal), 
which points to one or more conceptualizations, 
contained in the field "descr" of each nominal in 
the CDB. The adjectives contained in the noun phra- 
ses are also analyzed during this step. Each of 
them adds some conceptualizations which contribute 
to further individuate the nominal. During this 
step personal pronouns are identified as: 
Xi ~=--> ISA(HUMAN) 
Temporal and local adverbials are also analyzed 
in this phase in order to assign to the sentence 
conceptualization a time and place identification 
according to certain rules described in (Adorni 
et al.,'81). 
At the end of this step the sentence (i) is 
represented as follows: 
identifier nominal conceptualization 
Xl uomo (man) Xl <=~ISA(HUMAN) 
X2 capelli (hair) X2<==>ISA(HAIR) 
X3 Roma (Rome) X3~=>ISA(CITY) 
XS<==>NAME(ROME) 
X4 auto (car) X4<==>ISA(CAR) 
X5 Giuseppe (Joseph) X5<==>ISA(HUMAN) 
X5~-->NAME(JOSEPH) 
The sentence (i) can then be read: 
(2) "XI da X2 e' andato a X3 con X4 di X5" 
(XI from X2 is gone to X3 with X4 of X5) 
B. Analysis of Modifiers 
The simple phrases of a sentence can either fill 
conceptual cases of a main conceptualization, thus 
serving as 'picture producer' (PP), or further ind ! 
ON is spatial~el {AOHB} 
thought 
begin 
COORD X,Y OF P REFERReD_TO M = COORD X,Y OP 0 REFERREDTO M 
and 
P PART(A) and P NUM(>®) and O PART(B) 
end 
and 
herin 
COORD Z OF P ~ COOR9 Z OF Q and P ~U~T(1) 
end 
and 
begln B~=~PROPEL ~-- OBJ(FOPCE(H)) ~-- DIR((FROH{N~L))(TO(A))) 
end 
end. 
{exists, at least, ~ poin? P which is part Of the THOUGhT(A) and a 
point ~ which is par= of r.he THOUGHT(B) and for a.ny paL." Op points 
P and 0 is Z(P) >. ~(Q). More, there is an tssertion about the fact 
that the THOUGHT(B) suppor~ the THOUGHT(A)). 
FIG.5 - Example of Use of a Spatial Relationship in a Case Like "a man is on a chair" 
178 
viduate a PP. Therefore they can be classified ac- 
cording to whether they modify: 
a) the nominal that precedes(also not immediately); 
"i libri di Carlo" ^ 
(the books of Charles) 
b) the subject or object independently from their 
position; 
"Maria e' andata a Roma con Anna" ^ 
(Mary has gone to Rome with Ann) 
c) the action; 
"Maria e' andata a Roma con la macchina" ^ 
(Mary has gone to Rome with the car) 
C~IR IS descriptive 
~hOU~ht 
descr ISA(rd~.~TU~ )end/./'/ 
levels Of l: B3X_X 
2: CMAIR_I, 
end 
use Of 1: HL~(AN B~ING ON CHAZR 
m 
end 
Support Of I: HU!IC~8£INC~.~ 
2: ... 
end 
posl¢lon_of i: CF~IR REAR TABLE 
end 
color Of I: LIGHT BROWN 
2: ... 
end 
made of .: ~OCD 
2: ... 
end 
welgh: 2kg -:- 8kg end 
dynamic ~30; 
• max: 5Gc~,5~Cm. IC~Jcm; 
min: 35c=.35cm.8Ocm; 
end 
end. 
FZG.3 
C~.~ZR Is pr'otot)"we 
thOUg~ ... end. ) 
FIG.5 
HUMJL~BEING Is descelp~Ive\] 
thought ... end. . ) 
NF.A~ IS $vatlalrel 
t~ought ... end. ) 
FIG.6 - Definition of the Descriptive THOUGHT 
of a Chair 
The treatement of the modifiers in b) and 4) re- 
quires that the structure of the sentence is en- 
tirely known and cannot, in any case, be performed 
before the verb has been analyzed (subject and ob- 
ject are considered type c) modifiers). The modi- 
fiers in a), on the contrary, have a local role, 
limited to the PP they are to modify, and their 
relation to the sentence structure is marginal. 
They are, therefore,immediately associated to their 
corresponding nominals. In (2) "da X2" and "di X5" 
are of this kind and are consequently linked to 
X1 and X4 producing: 
(3) "XI e' andato a X3 con X4" 
(XI has gone to X3 with X4) 
In the "descr" field of THOUGHTs Xl and X4 the 
following information is added: 
X2 < .... PART OF(X1) 
X5 <===> OWNERSHIP(X4) 
The embodying of a modifier creates complex 
PPs or CLUSTERs. Each CLUSTER has as its HEAD a 
b) or c) modifier,a conceptual index node modified 
by the accessory concepts. 
In our example "l'uomo dai capelli neri", "a Roma", 
and "con l'auto di Giuseppe" are CLUSTERs, in which 
the head is always the leftmost nominal. 
The decision about the embodying of a modifier into 
its head is related to the classical problem of 
the placement of PP's. In fact, it is not always 
the case that a prepositional phrase modifies a 
conceptual index node; it is often possible that 
it has to be embodied into another accessory modi- 
fier, as in: 
"il libro dell'uomo dal cappotto blu" 
(the book of the man with the blue coat) 
If it is defined: 
md ---> the current phrase; 
md-i ---> the immediately proceeding phrase; 
md-2 ---~ the phrase that immediaZely proceeds 
md-l; 
the solution is obtained by recursively deciding 
wether md is to be embodied into md-i or md-2. Re- 
cursion is from the lower level. 
This decision is made by a set of standard general 
procedures associated to prepositions (di, da, con, 
per .... ) and adverbs (sopra, sotto, davanti, die- 
fro, ...). Non-standard specialized alternatives 
are activated by particular nouns and verbs in or- 
der to treat hidiosyncrasies. These procedures are 
written as three-steps programs, which accomplish 
the operations of: 
1-LOOKING for compatibility of certain features 
of md,md-l, and md-2. Typical features are superset 
and part-of relations of md's. A rule may state 
that "IF md has a part-of relation to md-2 THEN 
md may be embodied into md-2". Example: 
"il libro del bambino dalla copertina rossa" 
/---md-2 ...... md-I ........... md ............ / 
(the book of the child with red cover) 
2-Deciding whether MERGING can be performed. This 
is made by imposing further restrictions of the 
type described above. Also the main conceptual- 
ization and other linguistic peculiarities are 
taken into account. 
3-Actual LINKING. In our example, the structure: 
md <===> PART OF(md-i OR md-2) 
"l'uomo dai capelli grigi" 
/--md-I ...... md .......... / 
HAIR <===> PART_OF(MAN) 
is produced because md "capelli" can be part-of 
md "uomo". 
Should it not have been the case, the following 
structure would have been produced: 
(md-I OR md-2) < .... POSS(md) 
"l'uomo dal vestito scuro" 
/--md-i ....... md ......... / 
179 
(the man with the dark dress) 
MAN c===, POSS(DRESS) 
L~4PADA DA TA~DLO is descriptive 
thou~t 
descr 
X.e=COND~ USE ,~-.- OBJ (LAMPADA) 
and 
\]I4PLICAT(LA~ADA ON TAVOLO) 
end 
o.. 
end. 
(it is an object such that if x Use the 
lamp in a standard way,then the lamp is 
on the table) 
FIG.7 - THOUGHT of the Table Lamp 
C. Construction of the Main Conceptualization 
The nucleus of a main conceptualization can be 
associated in the CDB both to a noun indicating 
an action, state or change of state and a verb. 
In our example, we find the THOUGHT of fig. 8. A 
time identification always related to the 
present (T@), is taken from the syntactic analysis 
and connected to this conceptualization, thus 
resulting into: 
X ~===~ PTR~NS .... OBJ(X) .... DIR((FROM(Z))(TO(Z))) A 
and 
INSTR(CONC) 
and 
T1 ~ T@ 
If a lexical ambiguity arises, the features assumed 
by the nominals in the previous steps will help 
to desambiguate. 
A~ARE is descriptive | 
thou~t ,. 
descr 
X~.:==~PTR~--- OBJ(X)~--- DIR((FR(~(Y))(TO(Z)) 
and 
D~TR(C~C) 
end 
end. 
FIG.8 - THOUGHT of the verb "andare" (to go). 
At this step "splitting" of a conceptualization 
often occurs. In the sentence: 
"Giovanni d~ un colpo a Maria" 
(lit. John gives a blow to Mary) 
although two nuclei are present (d~ & colpo),never- 
theless the correct interpretation is "Giovanni 
colpisce Maria" (John hits Mary), instead of "Gio- 
vanni trasferisce il possesso dell'oggetto colpo 
a Maria" (John tansfers the ownership of the object 
'blow' to Mary)!!! 
We have observed that this phenomenon involves con- 
ceptualizations based on the primitives of "state", 
"action", and "spatial relationship" and relies 
only on the pairs ACTION-STATE, ACTION-SPATIAL RE- 
LATIONSHIP, and ACTION-ACTION. The regularities 
ruling the formation of these pairs have been found 
to depend only upon those conceptual primitives. 
This keeps the number of rules to be evaluated rea- 
sonably small, if compared with the number of CDB 
entries (~600 entries in the present implementa- 
tion (Adorni et al.,'81))~ 
An example will illustrate the mechanism of reduc- 
tion of the conceptual "splitting" as well as of 
disambiguation. 
The pair ACTION-SPATIAL RELATIONSHIP may be repre- 
sented by: 
"tirare su il braccio" ^ A 
ACTION SPATIAL RELATIONSHIP 
(lift the arm) 
The compound "tirare su" has the two meanings: 
- innalzare, alzare,...(lift,raise .... ); 
- confortare, dare sollievo psiehico,...(encourage, 
console .... ); 
which can be conceptualized respectively: 
X ..... PTRANS .... OBJ(Y) .... DIR ( (FROM (K))(TO(H) ) ) 
and 
((COORD Z OF H- COORD Z OF K) 
and 
R(X ..... PROPEL .... OBJ(Y) .... DIR((FROM(NIL)) 
(TO(NIL)))) ) 
X ~ ~ DO == 
S(Y(CHANGE STATE((FROM(HAPPINESS(N)}) 
(TO(HAPPINESS(N)))) ) ) 
The context helps disambiguation. 
In our example, the object of the spatial rela- 
tionship being a physical object, the first alter- 
native is selected. The rule performs a further 
control, discovering that the physical object is, 
in this case, PART OF(HUMAN); the PROPEL primitive 
is then substituted by the MOVE primitive. 
D. Case Fillin~ in the Main Conceptualization 
The next step performed by the semantic module 
is the filling of the conceptual cases of the main 
conceptualization with the THOUGHTs instantiated 
during the previous steps. Again, standard rules 
are associated to prepositions and adverbs and 
hidiosyncrasies are also treated. These rules make 
use of messages sent by the syntactic component 
and look at the conceptual syntax of the main con- 
ceptualization. Through these rules the cluster"con 
X4" turns out to be 'instrumental' and the follow- 
ing conceptualization is then produced: 
(4) X1 .... USE .... OBJ(X4) 
Since the filler of the instrumental case of the 
main conceptualization has to be a conceptual- 
ization, the rule activated by the "con" modifier 
fills the instrumental case with (4). 
In (3), 'a X3' is placed in the destination of the 
directive case of the main conceptualization, be- 
cause preposition 'a' is stated to indicate the 
180 
'destination' if the main conceptualization con- 
tains a PTRANS,PROPEL or MOVE,with empty directive 
case; otherwise it indicates 'state'. "Andare a 
Roma" is thus distinguished from "essere a Roma" 
(to be in Rome). The result, for our example, is: 
XI< .... PTRANS~--- OBJ(XI)~--- DIR((FROM(NIL)) 
(TO(IN X3)) ) 
The directive case,as shown in the above example 
is not simply filled with a md; it is filled with 
a "spatial_relationship-md" pair. This is a general 
rule for our system, emphasizing the change of coot 
dinates caused by an action. In our example this 
means that the primitive PTRANS has moved the ob- 
ject to a point whose coordinates are defined with 
in the city of Rome. The result of the analysis 
of (I) is given in Fig.9. 
X6 Is de$crlptLve 
thought 
XI ~---* PT.RAN$ ,~--*OB3 (XI) ,,~--D IR ( (FRON(NI L) ) (TO( I;\[ 13 ) ) 
T_T l.~_-- ( T I< T 
/ INSTRIXI¢.-~U$.~-- or-JlX 1) / ,o0// / 
• X4 is de~c,~iptive X3 is descPiptlve 
thOUght thOUgh: 
desor deScl- 
ISA(CAR) ISA(CITY) 
end NA~ ( RCI,IE ) 
•.. end 
en~ ... 
end. 
X2 iS descriptive X5 is descriptive 
t hOU~ : I thought 
descr 1 ~.escr 
: SA ( M-a- !R " ~ I -~A ( h--7~'l ) 
PAINT GFfXI) IPOSS(X4) 
end end 
c~;or of 
end 
end. 
r i 
i .., 
end. 
FIG.9 - Result of the Analysis of the Sentence (i) 
E. Conceptual Analysis of Complex Sentences 
The process of semantic interpretation is ap- 
plied to every clause in the sentence, identified 
by a verb or a noun indicating an action. Seg- 
mentation into such clauses or nominalized clauses 
is obviously performed by the syntactic component, 
which has also non-standard rules for specific 
classes of (modal) verbs like: dovere (must),volere 
(to want),potere (can),incominciare (to start) .... 
These verbs constitute a single main conceptual- 
ization together with the embedded infinitive. 
Simple composition rules have been defined to com- 
bine the meaning of clauses (sentences). 
Thus for conjunction, as in 
"si alzo',si mise il cappello eapri' la porta" 
(he stood up,put on its hat and opened the door) 
the main conceptualizations associated to every 
proposition are connected by an 'and' relationship. 
(si alzo') ......................... T1 
and 
(si mise il cappello) T2 >TI 
and 
(apri' la porta) T3 >T2 
A time indication is also associated to every main 
conceptualization to emphasize the execution order 
of every action. 
Conceptual analysis of each single clause (sen- 
tence) is activated by this top level structure 
and at the end the resulting conceptualizations 
are linked one to the other. 
V CONCLUSIONS 
In this paper a system for understanding a natu- 
ral language input to a scene generator has been 
described. It makes use of a conceptual dependency 
semantic model, substantially modified in as much 
as syntax is kept apart from semantic interpre- 
tation and a fully formalized dictionary is used, 
much more complex than the one embodied in Schank's 
theory. The dictionary is particularly oriented 
to the generation of scenes, and the stress is on 
the representation of the structure of objects. 
The awareness of the structure of the objects is 
often intimately related to our capability of under 
standing the. meaning of spatial relationships and 
other complex linguistic expressions. For instance, 
the meaning "the cat is under the car" is clear, 
even if it may depend on the state of the car, 
moving or parked; on the contrary, the sentence 
"the cat is under the wall" is not clear, unless 
the wall is crashed or it has a very particular 
shape.Our model tries to account t~is understanding 
activity by means of the following features: 
- an object is described at several levels of de- 
tails; in some cases, only a rough definition of 
the object dimensions can be sufficient, while 
in other cases a more sophisticated knowledge 
about the structure of the object itself is re- 
quired; 
- the characteristic features of an object are 
emphasized; the recognition of a feature allows 
the activation of particular rules and the gener- 
ation of hypotheses about the presence of an ob- 
ject; 
- the typical relationships among objects are 
described. 
The interaction between syntactic and semantic 
analyzers seems rather complex, but it provides 
some valuable solutions to certain crucial points 
of computational linguistics, like PP's placement, 
conceptual splitting, idioms and preassembled 
181 
The syntactic analyzer, working top-down, yelds 
a representation of the input sentence in which 
information about gender, number, person and tense 
are recorded and for each function such as subj, 
obj, time, etc.., the ccrresponding filler is iden- 
tified, or a list of fillers is given in case of 
ambiguity. These two kinds of information are 
exactly what is usefull for semantic interpretation 
and are picked up in various steps of the inter- 
action by the semantic analyzer in order to build 
the main conceptualization and to fill its role. 
Also MARGIE(Schank,'75) makes some use of syntactic 
knowledge distributed among lexical definitions 
of words. This solution gives the entire control 
to the semantic interpreter and no syntactic 
functional representation is used. It seems,however, 
that an intermediate step, keeping the syntactic 
output separate from the semantic one, has the 
advantage of avoiding the multiplication of single 
pieces of syntactic knowledge. It also provides 
a simpler way of dealing with syntactic variants 
of the same sentence and a help in identifying 
coreferences. 
The semantic interpreter works fundamentally 
bottom-up and, although much is still to be at- 
tempted, it seems that it can usefully cooperate 
with a top-down parser to find the correct inter- 
pretation. These practical advantages will be taken 
into account also in the future development of the 
system. In fact it seems that, although no definite 
solution has been given to many linguistic problems, 
the interaction between two fully developped mecha- 
nisms controlling each other can provide an indi- 
cation and a frame into which a more compact system 
can be built. 
In the present version of the system the inter- 
action between the two modules is strictly 
sequential. In a more compact analyzer, syntactic 
specialists, i.e. simplified pieces of grammar 
specialized in particular syntactic phenomena, will 
be called by semantic interpreter according to 
opportunity. This second version is still being 
designed. 
VI ACKNOWLEDGEMENTS 
The autors would like to thank Dr. Lina Massone 
for her contributions and assistance in the prepa- 
ration of this paper. 
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