0.0 INTRODUCTION 
A CASE FOR RULE-DRIVEN SEMANTIC PROCESSING 
Marcha Palmer 
Department of Computer and Information Science 
University of Pennsylanla 
The primary cask of semantic processing 
is to provide an appropriate mapping between 
the synCactlc consClCuanCs of a parsed 
sentence and the arguments of the semanclc 
predlcaces implied by the verb. This is 
known as the Alignment Problem.\[Levln\] 
Sectloo One of thls paper gives an 
overview of a generally accepted approach to 
semantic processing that goes through several 
levels of representation to achieve this 
mapping. Although somewhat inflexible and 
cumbersome, the different levels succeed in 
preservln S the context sensitive information 
provided by verb semantics. Section Two 
presents the author's rule-driven approach 
which is more uniform and flexible yet still 
accommodates context senslClve constraints. 
This approach is based on general underlying 
principles for syntactic methods of 
Incroduclns semantic arguments and has 
interesting implications for linguistic 
theories about case. These implications are 
dicuesed in Section Three. A system that 
implements this approach has been designed 
for and tested on pulley problem statements 
gathered from several physics text 
books.\[Palmer\] 
1.0 MULTI-STAGE SEMANTIC ANALYSIS 
A popular approach \[Woods\], \[Simmons\], 
\[Novak\] for assisnlng semantic roles Co 
syntactic coosClcueoCs can be described with 
three levels of representation - a schema 
level, a canonical level, and a predicate 
level. These levels are used to bridge the 
gap between the surface syncactlc 
representation and the "deep" conceptual 
represeoCatlon necessary for communicating 
wlth the Incernal database. While the 
following description of these levels may not 
correspond to any one Implementaclon in 
particular, It will give the flavor of the 
overall approach. 
I.i Schema Level The first level corresponds 
to the possible surface order configurations 
a verb can appear in. In a domain of 
equilibrium problems the sentence 
"A rope supports one end of a scaffold." 
could match a schema like "<physobJ> SUPPORTS 
<locpart> of <physobJ>". The word ordering 
here implies chec the first <physobJ> is the 
SUBJ and the <locpart> is the OBJ. Other 
likely schemes for sentences involving the 
SUPPORT verbs are "<physobJ> SUPPORTS 
<physobJ> AT <locpart>," "<physobJ> SUPPORTS 
<force>," "<physobJ> IS SUPPORTED," sod 
"<locpart> IS SUPPORTED."\[Novak\] Once a 
particular sentence has marched a schema, it 
is useful to rephrase the information in a 
more "canonical" form, so Chac a single of 
inference rules can apply Co a group of 
schemas. 
1.2 Canonical Level This intermediate level 
of representation usually consists of the 
verb itself, (or perhaps a more primitive 
semantic predicate chosen to represent the 
verb) and a list of possible roles, e.g. 
arguments to the predicate. These roles 
correspond loosely to a union of the various 
semantic types indicated in the schemas. The 
schemas above could all easily map into: 
SUPPORTS(<physobJ>l,<physobJ>2, 
<Iocpart>,<force>). 
The "canonical" verb representation 
found at this level bears certain 
similarities to a standard verb case frame, 
\[Simmons, Bruce\] in the roles played by the 
arguments to that predicate. There has been 
some controversy over whether or not any 
benefits are gained by labeling these 
arguments "cases" and aCtempting to apply 
linguistic generalities about case. 
\[Fillmore\] The possible benefits do not seem 
to have been realized, wlth a resulting shift 
away from explicit ties to case in recent 
work. \[Charnlak\], \[Wilks\] 
1.3 Predicate Level However, the implied 
relationships between the arguments still 
have to be spelled out, and thls is the 
function of our third and final level of 
representation. This level necessarily makes 
use of predicates chat can be found in the 
data base, and for the purposes of the 
program is effectively s "deep" semanclc 
representaClon. A verb such as SUPPORT would 
require several predicates in an equilibrium 
domain. For example, the "scaffold" sentence 
above could result in the followln S llst 
corresponding Co the general predlcaCes 
listed immediately below. 
"Scaffold" Example 
SUPPORT(rope,scaffold) 
UP(Fl,rope) 
UOWN(F2,scaffold) 
CONTACT(rope,scaffold) 
LOCPT(rtendl,rope) 
LOCPT(rtend2,scaffold) 
SAMEPLACE(rceodl,rtend2) 
General Predicates 
SUPPORT(<physobJ>l,<physobJ>2) 
UP(<force>l,<physobJ>1) 
OOWN(<force>2,<physobj>2) 
CONTACT(<physobJ>l,<physobJ>2) 
LOCPT(<locparc>l,<physobJ>l) 
LOCPT(<locpart>2,<physobJ>2) 
SAMEPLACE(<locpart>t,<locpert>2) 
125 
Producing the above list requires common 
sense deductions \[Bundyl about the existence 
of objects fllllng arguments chat do not 
correspond directly Co the canonical 
arguments, i.e. the two <locpt>s, and any 
arguments that were missing from the explicit 
seuteoce. For instance, in our scaffold 
example, no <force> was mentioned, and must 
be inferred. The usefulness of the canonical 
form is illustrated here, as It prevents 
tedious duplication of inference rules for 
slightly varying schemas. 
The relevant information from the 
sentence has now been expressed in a form 
compatible wlth some internal database. The 
goal of thls semantic analysis has been to 
provide a mapping between the original 
syntactic constituents and the predicate 
arguments in the final representation. For 
our scaffold example the following mapping 
has been achieved. The filling in of gaps in 
the final representation, although motivated 
by the needs of the database, also serves to 
rest and expand the mapping of the syntactic 
constituents. 
SUBJ <- rope <physobJ>l 
OBJ <- end <pbysobJ>2 
OFPP<- scaffold <locpart>2 
An obvious question at this point is 
whether or not the mappings from syntactic 
constituents to predicate arguments can be 
achieved directly, since the above 
multi-stage approach has at least three major 
disadvantages: 
1) It is tedious for the programmer co 
produce the original schemas, and the 
resulting amount of special purpose code is 
cumbersome. It is difficult . for the 
programmer to guarantee that all schemas have 
been accounted for. 
2) This type of system is not very 
robust. A schema that has been left out 
simply cannot be matched no matter how much 
it has in common with stored schemas. 
3) Because of the inflexibility of the 
system It is frequently desirable co add new 
Informaclon. Adding Just one schema, much 
less an entire verb, can be clme consuming. 
How much of a hindrance thls will be is 
dependent on the extent Co which the semantic 
information has been embedded in the code. 
The LUNAR project's use of a meanlns 
representation language greatly increased the 
efficiency of adding new information. 
The following section presents a system 
thaC uses syntactic cues at the semantic 
predicate level to find mappings directly. 
This method has Inceresclng implications for 
theories about cases. 
2.0 RULE-DRIVEN SEMANTIC ANALYSIS 
This section presents a system for 
semantic processin S that maps syntactic 
constituents directly onto the arguments of 
the semantic predicates suggested by the 
verb. In order Co make these assignments, 
the possible syntactic mappings must be 
associated with each argument place in the 
original semantic predicates. For instance, 
the only possible syntactic constituent that 
can be assigned to the <physobJ>1 place of a 
SUPPORT predicate is the SUBJ, and a 
<physobJ>2 can only be filled by an OBJ. But 
a <locpart> might be an OBJ or the object of 
an AT preposition, as in "The scaffold iS 
supported at one end." (The scaffold in this 
example is the syntactic subject of a passive 
sentence, so iC is also considered the 
logical object. For our purposes we will 
look on it as an OBJ). It might seem at 
first glance chat we would want to allow our 
<physobJ>2 to be the object of ao OF 
preposition, as in "The rope supports one end 
of the scaffold." But that is only true if 
the OFPP follows something llke a <locpart> 
which can be an OgJ in a sentence about 
SUPPORT. (Of course, Just any OPPP will not 
supply a <physobJ>2. In "The rope supports 
the end of greatest weight.", the object of 
the OPPP Is not a <physobJ> so could not 
satisfy <physobJ>2. The <physobJ>2 in thls 
case must be provided by the previous 
context.) 
It is this very dependency on the 
existence of other spmcific types of 
syntactic constituents chat was captured by 
the schemas mentioned above. It is necessary 
for an alternative system to also handle 
context sensitive constraints. 
2.1 Decision Trees The three levels of 
representation mentioned in Section One can 
be viewed as the bottom, middle and top of a 
tree. 
SUPPOET(p I ,p2) 
CONTACT(pl,p2) 
LOCPT (lpC 1 ,pl ) 
LOCPT(lpc2,p2) 
I J 
I 
SUPPORT(p I, p2, lpt, force) 
/ I \ 
/ J \ 
I 
SUBJ OBJ OPPP 
<physobJ> SUPPORTS <locpart> OF <physobJ> 
"The rope supports one and of the scaffold." 
126 
The inference rules that link the three 
levels deal mainly with any necessary 
renaming of the role an argument plays. The 
SUBJ of the schema level is renamed 
<physobJ>1 or pl at the canonical level, and 
is still pl at the predicate level. 
One way of viewing the schemas is as 
leaf nodes produced by a decision tree that 
starts at the predicate level. The levels of 
the tree correspond to the different 
syntactic constituents that can map onto the 
arguments of the original set of predicates. 
Since more than one argument can be renamed 
as a particular syntactic constituent, there 
can be more than one branch at each level. 
If a semantic argument might not be mentioned 
explicitly in the syntactic configuration, 
this also has to be expressed as a rule, ex. 
pl -> NULL. (Ex. "The scaffold is 
supported.") When all of the branches have 
been taken, each terminal node represents the 
set of decisions corresponding to a 
particular schema. (See Appendix A.) Note 
that the canonical level never has co be 
expressed explicitly. By working top down 
instead of bottom up unnecessary duplication 
of inference rules iS automatically avoided. 
The information in the original three 
levels can be stored equivalently as the top 
node of the decision tree along with the 
renaming rules for the semantic arguments 
(rewrite rules). This would reverse the 
order of analysis from the bottom-up mode 
suggested in section one to a cop-down mode. 
This uses a more compact representation, but 
would be computationally less efficient. 
Growing the entire decision tree every time a 
sentence needed to be matched would be quite 
cumbersome. However, if only the path to the 
correct terminal node needed to be generated, 
this approach would be computatlonally 
competitive. By ordering the decisions 
according to syntactic precedence, and by 
using the data from the sentence in question 
to prune the tree WHILE it is being 
generated, the correct decisions can usuallly 
be made, with the only path explored being 
the path to the correct schema. 
2.2 Context Sensitive Constraints Context 
sensltivity can be preserved by only allowing 
the p2->OPPP rule to apply after a mappin S 
for Iptl has been found, evidence that an 
Iptl->OBJ rule could have already applied. 
To test whether such a mapping has been made 
given a LOCPT predicate, it is only necessary 
tO see if the iptl argument has been renamed 
by a syntactic constituent. The renaming 
process can be thought of as an instantlatlon 
of typed variables, - the semantic arguments 
by syntactic constituents. \[Palmer, 
Galller, and Welner\] Then the following 
preconditions must be satisfied before 
applying the p2->OFPP rule: ( /\ stands for 
AND) 
p2->OFPP/ LOCPT(Iptl,p2) 
/\ not(varlable(iptl)) 
These preconditions will still need to 
be satisfied when a LOCPT predicate is part 
of another verb representation. Anytime a 
<locpart> is mentioned It can be followed by 
an OFPP introducing the <physobJ> of which It 
is a location part. This relationship 
between a <locparc> and a <physobJ> is Just 
as valid when the verb is "hang" or 
"connect." Ex. "The pulley is connected to 
the right end of the string." " The particle 
is hung from the right end of the string." 
These particular constraints are general to 
the domain rather than being restricted to 
"support'. This illustetes the efflclency of 
associating constraints with semantic 
predicates rather than verbs, allowing for 
more advantage to be taken of generalities. 
There is an obvious resemblance here to 
the notation used for Local Constraints 
grammars \[Joshi and Levy\]: 
p2->OFPP/ DOM(LOCPT) /\ 
LMS(Iptl) /\ not(var(iptl)) 
DOM - DOMinate, 
LMS - Left Most Sister 
It can be demonstrated that the context 
sensitive constraints presented here are a 
simple special case of their Local 
Constraints, since the dominating node is 
limited to being the immediate predicate 
head. Whether or not such a restricted local 
context will prove sufficient for more 
complex domains remains to be proven. 
2.3 Overview As illustrated above, our 
mappings from syntactic constituents to 
semantic arguments can be found directly, 
thus gaining flexibility and uniformity 
without losing context sensitivity. Once the 
verb has been recognized, the semantic 
predicates representing the verb can drive 
the selection of renaming rules directly, 
avoiding the necessity of an intermediate 
level of representation. The contextual 
dependencies originally captured by the 
schemes are preserved in preconditions that 
are associated with the application of the 
renaming rules. Since the renaming rules and 
the preconditions refer only to semantic 
predicates and arguments to the predicates, 
there is a sense in which they are 
independent of individual verbs. By applying 
only those rules that are relevant to the 
sentence in question, the correct mappings 
can be found quickly and efficiently. The 
resulting system is highly flexible, since 
the same predicates are used in the 
representation of all the verbs, and many of 
the preconditions are general to the domain. 
This facillitates the addition of similar 
verbs since most of the necessary semantic 
predicates with the appropriate renaming 
rules will already be present. 
127 
3.0 THE ROLE OF CASE INFORMATION 
Although the canonical level has often 
been viewed as the case frame level, doing 
away with the canonical level does not 
necessarily imply chat cases are no longer 
relevant to semantic processing. On the 
contrary, the importance here of syntacclc 
cues for introducing semantic arguments 
places even more emphasis on the traditional 
noclon of case. The suggestion is chat the 
appropriate level for case information is in 
fact the predicate level, and that most 
cradlClonal cases should be seen as arguments 
to clearly defined semantic predicates. 
These predicates are no~ merely the 
simple set of flat predicates indicated in 
the previous sections. There is an implicit 
structurihg to chat set of predicates 
indicated by the implications holding between 
them. A SUPPORT relationship implies the 
existence of UP and DOWN forces and a CONTACT 
relationship. A CONTACT relationship implies 
the existence of LOCPT's and a SAMEPLACE 
relationship between them. The set of 
predicates describing "support" can be 
produced by expanding the implications of the 
SUPPORT(pl,p2) predicate into UP(fl,pl) and 
DOWN(f2,p2) and CONTACT(pl,p2). 
CONTACT(pl,p2) is in turn expanded into 
LOCPT(Iptl,pl) and LOCPT(ipt2,p2) and 
SAMEPLACE(IpI,Ipt2). These deflniclons, or 
expansions, are represented as the following 
rewrite rules: 
supporc<->SUPPORT(pl,p2) 
SUPPORT(pl,p2)<-> 
UP(fI,pI)/\DOWN(f2,p2) 
/\CONTACT(pl,p2) 
CONTACT(pl,p2)<-> 
LOCPT(IpCI,pI)/\LOCPT(Ipt2,p2) 
/\SAMEPLACE(pl,p2) 
When "support" has been recognized as 
the verb, these rules can be applied, to 
build up the set of semantic predicates 
needed to represent support. If there were 
expansions for UP and DOWN they could be 
applied as well. As the rules are being 
applied the mappings of syntactic 
constituents to predicate arguments can be 
made at the same time, as each argument is 
introduced. The case information is not 
merely the set of semantic predicates or Just 
the SUPPORT(pI,p2) predicate alone. Rather, 
the case information is represented by the 
set of predicates, the dependencies indicated 
by the expansions for the predicates, and the 
renaming rules that arm needed to fled the 
appropriate mappings. The renaming rules 
correspond to the traditional syntactic cues 
for introducing particular cases. They are 
further restricted by being associated wlth 
the predicate context of an argument rather 
than the argument in IsolaClon. 
When this structured case information is 
used to drive semantic processing, It is not 
a passive frame that waits for its slots to 
be filled, but rather an active structure 
that goes in search of fillers for its- 
arguments. If these Instantiatlons are sot 
indicated explicitly by syntax, they must be 
inferred from a world model. The following 
example illustrates how the acClve case 
structure can also supply cases not mentioned 
explicitly in the sentence. 
3.1 Example Given a pair of sentences like 
"Two men are lifting a dresser. A rope 
supports the end of greatest weight." 
we will assume that the first sentence 
has already been processed. Having 
recognized that the verb of the second 
sentence is "support', the appropriate 
expansion can be applied co produce: 
SUPPORT(rope,p2) 
This would in turn be expanded to: 
UP(fl,rope) 
DOWN(f2,p2) 
CONTACT(rope,p2) 
In expanding the CONTACT relationship, 
an 1ptl for "rope" and a p2 for "end" need co 
be found. (See Section Two) Since the 
sentence does not supply an ATPP that might 
introduce an lpcl for the "rope" and since 
there are no more expansions that can be 
applied, a plausible inference must be made. 
The lptl is likely co be an endpoinc Chat is 
not already in contact with something 
else.This implicit object corresponding to 
the free end of the rope cam be name 
"ropend2." The p2 is more difficult. The 
OFPP dome ant introduce a cphyaobJ>, although 
It does specify the "and" more precisely. 
The "end" must first be recognized as 
belonging Co the dresser, and then as being 
its heaviest end, "dresserend2." This is 
really an anaphora problem chat cannot be 
decided by the verb, and could in fact have 
already been handled. Given "dreseerend2", 
it only remains for the "dresser" Co be 
inferred as the p2 of the LOCPT relationship, 
using the same principles that allow an OFPP 
to introduce a p2. The final set of 
predicates would be 
SUPPORT(rope,dresser) 
/1\ 
/1\ / I \ 
UP(fl,rope) \] DOWN(f2,dreeser) 
I 
CONTACT(rope,dresser) 
/1\ 
/l\ 
/ I \ 
LOCPT(ropend2,rope)LOCPT(dreeserend2,dresser) 
I \[ 
SAMZPLACE(ropmud2,dresserend2) 
Both the ropeod2 and "dresser" were 
supplied by plausible reasoning using the 
context and a world model. There are always 
many inferences that can be drawn when 
processing a single sentence. The detailed 
nature of the case structure presented above 
gives one method of regulating ~hls 
inferencing. 
128 
3.2 Associations wlt____~h llnsulstlcs A recent 
trend in linguistics co consider cases as 
&rguments to thematic relations offers a 
surprising amount of support for this 
position. Without denying the extremely 
useful tles between syntactic constltuencs 
sod semantic cases, Jackendoff questions the 
abillcy of case to capture complex semantic 
relationships. \[Jackendoff\] His main 
objection is that standard case theory does 
not allow a noun phrase to be assigned more 
than one case. In examples llke "Esau traded 
hls birthright (to Jacob) for a mess of 
pottage," Jackendoff sees two related 
actions: "The first is the change of hands 
of the birthright from Esau to Jacob. The 
direct object is Theme, the sub~ect is 
Source, and the to-object is Goal. Also 
there is what I will call the secondary 
action, the changlnS of hands of the mess of 
pottage in the ocher direction. In this 
action, the for-phrase is Secondary Theme, 
the subject is Secondary Goal, and the 
to-phrase is Secondary Source." \[p.35\] This, 
of course, could not be captured by a 
Fillmore-llke case frame. Jackendoff 
concludes that, "A theory of case grammar in 
which each noun phrase has exactly one 
semantic function in deep structure cannot 
provide deep structures which satisfy the 
stron S Katz-Postal Hypothesis, that is, which 
provide all semantic information about the 
sentence." Jackendoff is sot completely 
dlscardln E case information, but rather 
suggesting a new level of semantic 
representation that tries to incorporate some 
of the advantages of case. Making 
constructive use of Gruber's system of 
thematic relationships \[Gruher\], Jackendoff 
postulates "The thematic relations can now be 
defined in terms of \[these\] semantic 
subfunctlons. Agent is the argument of CAUSE 
chat is an individual; Theme Is the argument 
of CHANGE that is an individual; Source and 
Goal are the initial and final state 
arguments of CHANGE. Location will be 
defined in terms of a further semanclc 
function BE thac takes an individual (the 
Theme) and a state (the Locatlon). \[p.39\] 
Indeed, Jackendoff is one example of a 
trend noted by Janet Fodor She points out 
chat "it may be more revealing to regard the 
noun phrases which are associated in a 
variety of case relations with the LEXICAL 
verb as the arguments of the primitive 
SEMANTIC predicates into which It is 
analyzed. These semantic predicates 
typically have very few arguments, perhaps 
three at the most, but there are a lot of 
them and hence there will be a lot of 
distinguishable "case caCesorles.'(Those 
which Fillmore has identified appear to be 
those associated wlth semantic components 
that are particularly frequent or prominent, 
such as CAUSE, USE, BECOME, AT.)" \[p.93\] 
Fodor summarizes with, "As a contribution CO 
semantics, therefore, it seems best to regard 
Fillmore's analyses as merely scepplng stones 
on the way Co a more complete specification 
of the meanings of verbs." The one loose end 
in thls neat summation of case is its 
relation to syntax. Fodor conclnues, 
"Whether there are any SYNTACTIC properties 
of case categories that Fillmore's theory 
predicts but which are missed by the semantic 
approach is another question...." 
It Is the thesis of thls paper that 
these synCactlc properties of case categories 
are the very cues that are used to drive the 
filling of semantic arguments by syntactic 
constituents. Thls system also allows the 
same syntactic constituent to flll more than 
one argument, e.g. case category. The 
following section presents further evidence 
chat thls system could have direct 
implications for linguistic theories about 
case. Although it may at first seem that the 
analysis of the INSTRUMENT case contradicts 
certain assumptions that have been made, it 
actually serves to preserve a useful 
disctinction between marked end unmarked 
INSTRUMENTS. 
3.3 The INSTRUMENT Case 
The cases necessary for "support" were 
all accomodated as arguments to semantic 
primitives. Thls does not imply, however, 
that cases can never play a more important 
role In the semantic representation. It is 
possible for a case to have Its own expansion 
which contains information about how semantic 
predicates should be structured. There is 
quite convincing evidence in the pulley 
domain for the influential effect of one 
particular case, 
In thls domaln INSTRUMENTS are 
essentially "intermediaries" in "hang" and 
"connect" relationships. An <inter>medlary 
is a flexible llne segment that effects a 
LOCATION or CONTACT relationship respectively 
between two physical objects. Example 
sentences are "A particle is hung by a string 
from a pulley," and "A particle is connected 
to another particle by a string." The 
following rewrite rules ere the expansions 
for the "hang" and "connect ° verbs, where the 
EFFECT predicate wlll have Its own expansion 
corresponding to the definition of an 
intermediary. 
han S <-> EFFECT(lnter,LOCATION(pI,Ioc)) 
connect <-> EFFECT(Inter,CONTACT(pI,F2)) 
Application of these rules repectlvely 
results in the following representation for 
the example sentences: 
EFFECT(string,LOCATION(perticlel,pulleyl)) 
EFFECT(strlng,CONTACT(parrlclel,psrtlcle2)) 
129 
The expansion of EFFECT itself is: 
EFFECT(inter, REL(argl,arg2)) <-> 
REL(argl,inter), 
REL(inter,arg2)) 
where REL stands for any semantic 
predicate. The application of this expansion 
to the above representations results in: 
LOCATION(particlel,string) 
LOCATION(strlng,pullayl) 
and 
CONTACT(particlel,strins) 
CONTACT(sCrlng,partlcla2) 
These predicates can then be expanded, 
with LOCATION bringing in SUPPORT and 
CONTACT, and CONTACT bringing in LOCPT. 
3.4 Possible Implications There seams to be a 
direct connection between the previous 
expansion of intermediary and the analysis of 
the INSTRUMENT case done by Beth Levln at 
MIT.\[Levln\] She pointed out a distinct 
difference in the use of the same INSTRUMENT 
in the following two sentences: 
"John cut his foot with a rock." 
"John cut his foot on a rock." 
In the first sentence there is an 
implication that John was in some way 
"controlling" the cutting of his foot, and 
using the rock to do so. In the second 
sentence there is no such implication, and 
John probably cut his foot accidentally. The 
use of the "with" preposition marks the rock 
as an INSTRUMENT. that is being manipulated 
by John, whereas "on" introduces an unmarked 
INSTRUMENT with no implied ralationshion to 
John. It would seem that something llke the 
expansion for EFFECT could help to capture 
part of what is being implied by the 
"control" relationship. Bringing in the 
transitivity relationship makes explicit a 
connection between John and the rock as well 
as between the foot and the rock. ~n the 
second sentence only the connection between 
the foot and the rock is implied. The 
connection implied here is certainly more 
complicated than a simple CONTACT 
relationship, and would neccsssitate a more 
detailed understanding of "cut." But the 
suggestion of "control" is at least indicated 
by the embedding of the CUT predicate within 
EFFECT and CAUSE. 
CAUSE(John,EFFECT(rock,(CUT(foot-of-John))) 
The tie between the AGENT and the 
INSTRUMENT is another implication of 
"control" that should be explored. 
That the distinction between marked and 
unmarked INSTRUMENTS can be captured by the 
EFFECT relationship is illustrated by the 
processing of the following two sentences: 
"The particle is hung from a pulley by a 
string." 
"The particle is hung on a string." 
In the first sentence an "inter" (a 
marked INSTRUMENT) is supplied by the BYPP, 
and the following representation is produced: 
EFFECT(string,LOCATION(partlcle,pulley)) 
In the second sentence no "inter" is 
found, and in the absence of an "inter" the 
EFFECT relationship cannot be expanded. The 
LOCATION(particle,strlng) predicate is left 
to stand alone and is in turn expanded. (The 
ONPP can indicate a "lot. °) 
The intriguing possibility of verb 
independent definitions for cases requires 
much more exploration. \[Charniak\] The 
suggestion here is that a deeper level of 
representation, the predicate level, is 
appropriate for investigating case 
implications, and that important cases llke 
AGENTS and INSTRUMENTS have implications for 
mats-level structuring of those predicates. 
3.5 Summary In summary, there is a surprising 
amount of information at the semantic 
predicate level that allows syntactic 
constituents to be mapped directly onto 
semantic arguments. This results in a 
semantic processer that has the advantage of 
being easy to build and more flexible than 
existing processers. It also brings to light 
substantial evidence that cases should not be 
discarded but should be reexamined with 
respect to the roles they play as arguments 
to semantic predicates. The INTERMEDIAKY 
case is seen to play a particularly important 
role having to do not with any particular 
semantic predicate, but with the choice of 
semantic predicates in general. 
References 
\[I\] Bruce, B., Case system for natural 
language, "Artificial Intelligence," Vol. 6, 
No. 4, Winter, pp. 327-360. 
\[2\] Bundy, et-al, Solving Mechanics Problems 
Using Mats-Level Inference, Expert Systems i_.~n 
the Micro-Electronic ARe, Michia, D.(ed), 
Edinburgh University Press, Edinburgh, U.K., 
1979. 
\[3\] Charnlak, E., A brief on case, Working 
Paper No.22, (Castagnola: ~nstitute for 
Semantics and Cognitive Studies), L975. 
{4\] Fillmore, C., The case for case, 
Universals In Linguistic Theory, Bach and 
Harms (eds.) New York; Holt, Rinehart and 
Winston, pp. 1-88. 
\[5\] Fodor, Janet D., Semantics: Theories of 
Meanin~ in Generative Grammar, Language and 
Thought Series. Thomas Y. Crowell Co., Inc., 
1977, p. 93 
130 
\[6\] Gruber, 
Syntax and 
Co., 1976. 
J.S., Lexlcal Structures in 
Semantics, North-Holland Pub. 
\[7\] Jackendoff, R.S., Semantic Interpreter i_nn 
Generative Grammar , HIT Press, Cambridge, MA, 
1972, p. 39. 
\[8\] Levln, B. "Instrumental With and the 
Control Relation in English," HIT Master*s 
Thesis, 1979. 
\[9\] Novak, G.S., Computer Understanding of 
Physics Problems Stated in Natural 
Language,Amerlcan Journal of CompuCatlonal 
Linguistics, Microfiche 53, 1976. 
\[I0\] Palmer, M., Where to Connect? Solving 
Problems in Semantics, DAI Working Paper No. 
22, University of Edinburgh, July 1977. 
\[11\] Palmer, M., "Driving Semantics for a 
Limited Domain," Ph.D. Thesis, forthcoming, 
University of Edinburgh. 
\[12\] Palmer, H., Galller, J., and Weiner, J., 
Implementations as Program Specifications: A 
Semantic Processer in Prolog, (submitted 
IJCAI, Vancouver, August 1981). 
\[13\] Simmons, R.F., Semantic Networks: Their 
Computation and Use for Understanding English 
Sentences, Computer Models of Thought and 
Language, Schank and Colby (eds.) San 
Francisco: W.H. Freeman and Co., 1973. 
\[14\] Wilks, Y., Processing Case, "American 
Journal of Computational Linguistics," 1976. 
\[15\] Woods, W.A., Semantics and 
Quantification in Natural Language Question 
Answering, BEN Report 3687, Cambridge, Mass, 
November 1977. 
APPENDIX A 
/ 
p2 -> OgJ/ / 
SUPPORT(SUBJ,OBJ) 
/\ CONTACT(SUBJ,OBJ) 
/\ LOCPT(IptI,SUBJ) 
/\ LOCPT(!pt2,OBJ) 
l 
ipc2 -> ATPP 
i 
SUPPORT(SUBJ,OBJ) 
/\ CONTACT(SUBJ,OBJ) 
/\ LOCPT(IptI,SUBJ) 
/\ LOCPT(ATPP,OBJ) 
I 
I 
SUBJ I 
SUPPORT(pl,p2) /\ CONTACT(pI,p2) /\ 
LOCPT(lpCl,pI) /\ LOCPT(lpc2,p2) / 
pl -> SUBJ / / 
SUPPORT(SUBJ,p2) /\ CONTACT(SUBJ,p2) 
/\ LOCPT(IptI,SUBJ) /\ LOCPT(Ipt2,p2) 
\ 
\ lpt2 -> OBJ \ 
SUPPORT(SUBJ,p2) /\ 
CONTACT(SUBJ,p2) /\ 
LOCPT(lptl,SUBJ) /\ 
LOCPT(OBJ,p2) \ 
\ p2 -> OFPP \ 
SUPPORT(SUBJ,OFPP) /\ 
CONTACT(SUBJ,OPPP) /\ 
LOCPT(1ptl,SUBJ) /\ 
LOCPT(OBJ,OPPP) \ 
\ 
OBJ ATPP \ 
<physobj> SUPPORTS <physobJ> AT <locpart> \ \ 
SUBJ \ OBJ OFPP 
<physobJ> SUPPORTS <locparC> OF <physobJ> 
\ 
\ pl -> NULL \ 
SUPPORT(pl,p2) /\ CONTACT(pI,p2) 
/\ LOCPT(lptl,pl) /\ LOCPT(lpC2,p2) / \ 
/ \ 
etc. etc. 
131 

