An Integrated Understander 
Roger C. Schank 
Michael Lebowitz 
Lawrence Birnbaum 
Department of Computer Science 
Yale University 
New Haven, Connecticut 06520 
A new type of natural language parser is presented. The idea behind this parser is 
to map input sentences into the deepest form of the representation of their meaning 
and inferences, as is appropriate. The parser is not distinct from an entire understand- 
ing system. It uses an integrated conception of inferences, scripts, plans, and other 
knowledge to aid in the parse. Furthermore, it does not attempt to parse everything it 
sees. Rather, it determines what is most interesting and concentrates on that, ignoring 
the rest. 
1. Overview of Conceptual Dependency Parsing 
Over the course of the last ten years, researchers 
in our project have designed and programmed a 
large number of parsers. The task of these parsers 
was the initial mapping of natural language into an 
internal representation. (Note that the first phase of 
the understanding process traditionally refers to the 
discovery of the syntactic form of the input. Howev- 
er, the term "parsing" can just as meaningfully be 
applied to whatever the first phase of understanding 
might be.) In this paper we will discuss some of the 
problems which have arisen in the development of 
parsers, and present a new theory of the way pars- 
ing works in the normal reading process. We will 
describe a program which implements this theory 
and understands newspaper stories about terrorism. 
All our parsers were programs that mapped Eng- 
lish sentences into the Conceptual Dependency 
(CD) representation of their meaning. Underlying 
their construction was always the methodological 
assumption that the parsing algorithm that they were 
to employ was to be as psychologically correct as 
possible. Thus, our parsers are intended to model 
the way we believe people parse. This methodologi- 
cal assumption brought with it an operating principle 
which was (with one exception to be discussed later) 
always followed, namely that the parsing algorithm 
was a left-to-right, one-pass operation without back- 
tracking. These parsers were not designed to handle 
true "garden path" sentences where people have to 
backtrack. 
The first parser that we worked on (Schank and 
Tesler, 1969), used what we called "realization 
rules" to map English syntactic structures into CD. 
(This term was taken from Lamb (1966) and signi- 
fied that we were mapping from one linguistic level 
to another.) The primary problem with this parser 
was that it violated our methodological goal of mod- 
eling human processes. For many English sentences 
that were ambiguous, the algorithm we used exhibit- 
ed no clear preference for one interpretation over 
another, even though people clearly had such prefer- 
ences. 
In Schank et al. (1970) we proposed a solution 
to remedy this problem in the design of a new par- 
ser which we called SPINOZA II. SPINOZA II was 
to use the CD representation itself to drive the 
parse. That is, during the parsing process, the mean- 
ing that had been understood up to any point would 
help in the determination of the meaning of the rest 
of the sentence. This idea brought with it the con- 
comitant idea that, since meaning would be driving 
the parse, we really might not have to rely very 
much on syntax to do our parsing. (Wilks (1973) 
was working on a similar idea at the same time and 
his view helped to support our own belief in the 
feasibility of the idea.) We did not believe that we 
could avoid syntax altogether. Rather, it was our 
view that relying on meaning considerations first 
would drastically reduce our parsers' dependence on 
syntax. 
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American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 13 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
While these parsers were being developed, other 
researchers were devising methods for parsing natu- 
ral language input into an internal representation. 
Most of these methods concentrated on the syntax 
of sentences. One very popular technique has been 
the Augmented Transition Network (ATN). Parsers 
of this sort have been discussed in Thorne, et. al. 
(1967), Bobrow and Fraser (1969), Woods (1969) 
and Kaplan (1975). A parser strongly related to 
ATN's is in Winograd (1972). ATN's have tended 
to deal primarily with syntax, perhaps occasionally 
checking a few simple semantic properties of words. 
Even more closely tied to syntax are the parsers 
based on Transformational Grammar, such as Plath 
(1974). A more recent parser which views syntax as 
an isolatable sub-part of language understanding is 
in Marcus (1979). The important thing to note 
about all of these programs is that they view syn- 
tactic parsing as a process totally isolated from the 
rest of understanding. Syntax drove these systems, 
although in some cases the syntactic parser was al- 
lowed to request semantic information. Some speech 
understanding systems, Hearsay-II in particular 
(CMU Computer Science Speech Group, 1976) use 
a more integrated approach, but they are only mar- 
ginally concerned with the level of processing that 
we are interested in. Our view has always stressed 
the integration of semantics and syntax in parsing. 
SPINOZA II was only partially finished when it 
was abandoned for reasons other than academic 
ones. A few other attempts were made at starting it 
up again until Chris Riesbeck designed a parser that 
was similar in spirit, but different in form from SPI- 
NOZA II (see Riesbeck, 1975). His program was 
based on what he termed requests, a form of prod- 
uctions (see Newell, 1973). Requests were activat- 
ed whenever expectations about some syntactic or 
semantic information could be made, and were exec- 
uted if the expectations they embodied came true. 
Thus, expectations guided the parse, making 
Riesbeck's system, later called ELI, very top down 
(see Riesbeck and Schank, 1976). 
ELI was used as a front end to SAM our script- 
based understanding system (Schank et al., 1975 
and Cullingford, 1978), and was combined with 
Gershman's (1977) work on noun groups to provide 
a parser that could handle very complex sentences. 
One of the major problems with ELI is its fragili- 
ty. Granger (1977) designed FOUL-UP, an adjunct 
to ELI which determines the meaning of unknown 
words in context, and this produced a more robust 
parser. But, in actual day-to-day use, students have 
often found it simpler to design special purpose par- 
sers patterned after EL/ that are less cumbersome 
and easier to use. Carbonell's (1979) POLITICS 
program, a model of subjective understanding of 
political events, for example, uses a parser that is 
similar to ELI, but was written by Carbonell him- 
self. 
Perhaps the most important feature of 
Carbonell's work is that it has pointed out to the 
rest of us a major flaw in our reasoning behind the 
design of large understanding systems. We have 
always leaned in the direction of modularity in the 
design of our programs, both because this has al- 
ways been considered good programming style, and 
because, since our systems are very large, each sepa- 
rate module has often been the work of a different 
person. 
But, this modularity has caused a number of 
problems. Any understanding system that we build, 
for example, should ideally use ELI as a front end. 
But ELI is a very large and cumbersome program to 
work with. Furthermore, there is another practical 
problem, namely that the vocabulary for any new 
domain to be handled by some system we set up is 
unlikely to be already present in ELI. Since in ELI 
the definitions of words are in a sense programs 
themselves, any new system will require the writing 
of a large part of its parsing program from scratch in 
any case. This practical problem leads to a much 
more interesting issue. In the same way that we real- 
ized years ago that it was important to take advan- 
tage of the power of the CD representations availa- 
ble to us to build a more integrated parsing system, 
any new parser designed for a new system should, in 
principle, take advantage of the higher level under- 
standing processes that are a part of the new sys- 
tem. Thus, POLITICS can parse more effectively if 
it can use not only the partially constructed CD 
representation of what it has already understood, 
but also its place in the ideology it is using, its over- 
all significance, and so on. That is, modularity is, in 
an important sense, a disadvantage. Why not capi- 
talize on everything that is available to help parsing 
along? People are no more likely to use only syntax 
and some particular notions of meaning (but not 
others) to help in the parsing than they are to use 
only syntax. Understanding is a completely inte- 
grated process. The idea of building modular sys- 
tems has hampered advances in parsing, because the 
full range of our knowledge should obviously be 
available to help disambiguate, find appropriate 
word senses, and just as importantly (as we shall see 
later in this paper) to help us know what to ignore. 
It should be emphasized what we mean when we 
say that modularity was a handicap in parser devel- 
opment. Clearly from a programming point of view 
our parsers must be modular. However, if modules 
seem to be spending all of their time communicating 
with each other, then the particular modularization 
scheme must be suspect - the modules really form an 
integrated unit. Since communicating amongst mo- 
dules tends to be hard, the tendency is to avoid it. 
14 American Journal of Computational Linguistics. Volume 8, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz. and Lawrence Birnbaum An Integrated Understander 
This can result in processes which should interact 
strongly becoming isolated from each other. This is 
what happened with the modularization strategy we 
have described. 
2. Paying Attention To Less Than Everything 
One of the major problems with SAM, and also 
with ELI as a part of SAM, was its inability to han- 
dle texts for which it was unprepared. A new vocab- 
ulary item, domain of discourse, or previously unen- 
countered syntactic construction could, and often 
would, throw things into disarray. One of the out- 
puts produced by SAM was a summary of what it 
had read. It seemed to us that we could produce 
essentially the same output with a much more robust 
and much faster program, FRUMP (DeJong, 1977). 
FRUMP does not process every word of every story 
that is input. Rather, it has embodied within it a 
theory of skimming that guides it in what it is read- 
ing. FRUMP skims for what it is interested in - usu- 
ally the items of information it wishes to include in 
its summary for any particular domain that it has 
knowledge about. FRUMP is thus a highly top-down 
system and for this reason it cannot be considered 
as a replacement for SAM. SAM could, in principle, 
respond to inputs it was unprepared for, although in 
practice this did not happen very often! FRUMP 
cannot respond to aspects of stories it is unprepared 
for; but then neither is it unable to process such 
stories at all. 
For our purposes here however, what is particu- 
larly interesting about FRUMP is that it is an exam- 
ple of a working, robust, integrated (that is, non- 
modular) system. FRUMP's parser is virtually indis- 
tinct from its inferencer. The reason is simple. 
FRUMP knows what it needs to find in a story. It 
has rules for how to find these things, which can be 
either inference rules or parsing rules. But such 
rules are really just the low-level manifestations of 
higher level decisions that have been made on the 
basis of many considerations, only some of which 
are related to parsing. FRUMP works as well as it 
does because its interests guide what it looks for. It 
can ignore what it is not interested in and concen- 
trate on what it wants to know. 
Now let's consider how a normal, literate adult 
reads a story, a newspaper story, for instance. We 
have considered seriously the question of whether a 
human reads in detail, like SAM, or skims, like 
FRUMP, in his normal reading mode. And, although 
we possess no hard evidence one way or the other, 
we now feel that a human is more FRUMP-like than 
we previously believed. If this is true, it has impor- 
tant implications about what a parser ought actually 
to look like. We are not suggesting that FRUMP's 
parser is adequate. Clearly it is not, as it misses sig- 
nificant aspects of many stories. On the other hand, 
some kind of combination of FRUMP parsing and 
ELI parsing might make for a very powerful and 
robust system for story understanding. 
3. Time of Processing in Parsing 
One of the major factors to be considered in 
discussions of the design of a human-like parser is 
the speed with which humans can read text. Consid- 
ering all the inferences and bringing in of back- 
ground knowledge and other problems that an un- 
derstander must deal with in the course of reading 
or listening to a sentence, people are very fast at the 
job. They finish understanding, for the most part, 
as soon as the sentence they are hearing is finished 
being uttered. This implies that the amount of time 
that they have available for inferencing and knowl- 
edge application cannot wait until the end, after 
parsing is finished. Rather, such additional process- 
es must be going on at the same time as parsing is 
going on. This is confirmed by psychological evi- 
dence such as Marslen-Wilson (1975) which uses 
errors in the shadowing of sentences to show high- 
level processing must be occurring throughout the 
reading of a sentence. Such a conclusion certainly 
makes the argument we were stating above much 
more significant. It implies that human processes are 
highly integrated. That is, people must be inferring 
from the early parts of a sentence before they even 
hear the latter parts of the sentence. If this is so, 
then it also follows that people will make use of 
whatever else they discover, thus allowing word 
sense identification, etc., to be affected by higher 
level processes. Thus, as models of human process- 
ing, parsers that first do their job completely and 
then send their results off to inferencers make no 
sense. 
There is a further consequence to this as well. 
We must ask ourselves when this non-parsing type 
processing takes place. There are two possible an- 
swers. Either people employ parallel processes and it 
all goes on at the same time, or if processing is seri- 
al, space must be being made to do this work at the 
expense of something else. This something else is 
likely to be the complete processing of every word 
that is seen. That is, in the serial view, not all words 
are equal. Some words get a lot of the processing 
time (those that have great syntactic, semantic and 
inferential importance for example) and others hard- 
ly get noticed. 
Now the question of whether the serial or paral- 
lel explanation is correct is really not resolvable 
here. However, even with some parallel processing, 
it seems plausible that the total processing capability 
available at any one time for use in understanding 
must have some bounds, and that the speed of input 
must often overwhelm those bounds. Thus we are 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 15 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
still left with the necessity of processing some words 
at the expense of others. 
The serial explanation, then, presumes a model of 
parsing which is in some places incomplete. As we 
have discussed elsewhere (Sehank, 1975, for in- 
stance), the process of doing a complete parse is 
extremely complex. Simply stated, it takes n milli- 
seconds to read a word and it takes m milli-seconds 
to completely process a word. Since it seems quite 
likely that m is much larger than n in ordinary 
speech and reading, and since words come in 
streams in ordinary speech and reading, then it is 
obvious that people cannot be completely processing 
every word they hear. What is more likely the case 
is that they are deciding what to pay serious atten- 
tion to and what to pay casual attention to as they 
go. Such decisions can be explained on the basis of 
many factors. One most obvious one is interest. 
That is, people are liable to pay attention to (that is, 
devote their processing time to) what interests them. 
We have discussed the concept of interest and its 
ramifications for the inference problem in Schank 
(1978). The main conclusion there was that infer- 
ence is controlled by interest. This is likely also to 
be true in this revised view of the parsing process 
then because we are now viewing the entire under- 
standing process as an integrated phenomenon. 
Consider the following sentence: 
A small twin engine airplane carrying federal 
marshals and a convicted murderer who was 
being transported to Leavenworth crashed 
during an emergency landing at O'Hare Air- 
port yesterday. 
Intuitively, some parts of this sentence are more 
interesting than others. But more than that, it is 
crucial, according to the idea stated above with re- 
spect to the amount of processing time available, 
that the processing of some words must take less 
time than the time it takes to read or hear them. 
Now at first glance this may seem a bit bizarre. 
How can a word be processed in less time than it 
takes to read or hear it if reading or hearing it is a 
part of that processing? Yet we are in precisely this 
paradoxical situation if we hold to the idea that the 
processing of any one word in a sentence can take 
longer than the time it takes to read or hear it, since 
it takes no longer to process an entire sentence than 
it does to hear it and since the individual words 
come in at such a rate that there is no time between 
them in which to process. (This is obviously the 
case since just finding the word boundaries in a sen- 
tence is a very complex task because the speech 
stream is continuous.) 
Since the amount of processing time available is 
limited by the rate of flow of the input (which is 
continuous for speech), then some words are proba- 
bly not being processed at all (or in any case they 
are processed so partially that they are hardly seen). 
Since the most important words often come at the 
end of a phrase, the preceding words may be virtual- 
ly ignored until they can be 'gathered up' right to 
left. Then top down processing helps the understan- 
der know what to ignore. According to this scheme, 
words are stored in a buffer and virtually ignored 
until a word that initiates processing is found. When 
such a word is found, the words in the buffer are 
gathered up and their analysis completed. Words 
which initiate processing usually appear at the end 
of phrases or breath groups. 
In order to process a noun phrase such as "small 
twin engine airplane" then, we must assume that a 
processor virtually ignores all the words until 
'airplane', simply marking their existence in short 
term memory for retrieval after the head noun is 
found. Once we know that 'airplane' is the subject 
of the sentence, expectations can be generated that 
allow us to have a better idea of what to look for 
(and therefore of what to ignore). For example, the 
meaning of 'carrying' can be virtually ignored, since 
as we are only beginning to recognize what word it 
is, we hear about the marshals and the murderer and 
decide to pay attention to those items. 
The point here is that we are really not seeing 
things one word at a time, but rather since we are 
seeing a continuous stream we can pick out what we 
find interesting, go back to discover just those rela- 
tionships that connect together what we are interest- 
ed in and virtually ignore the rest. Do we care that 
the verb 'carrying' was used instead of 'containing', 
or that the construction used was not "in which they 
were flying"? We have already predicted that the 
relationship between the people and the airplane 
was containment because that is what it ordinarily 
is. We need only confirm the fact that nothing con- 
tradicts this prediction and this can be done on the 
fly. Under this theory there is little wonder at the 
fact that understanders frequently cannot remember 
the actual words that they read. They may never 
have actually read them at all! 
A theory of partial parsing then, says that most 
words are barely noticed until some reason is found 
to pay attention to them. A major issue in our theo- 
ry then is how we know what words we must pay 
attention to and begin to process seriously. 
If certain elements of language are skipped en- 
tirely, or processed only slightly in the course of 
normal understanding, one might ask why they ap- 
pear at all. Why doesn't the author just omit them? 
The answer to this is that the same story can be 
understood by different readers with varying degrees 
of completeness. It is possible for a reader paying 
attention to every detail of a story to discover 
nuances that a partial understander like the one we 
16 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
are describing would miss. However, it is our belief 
that most of the time, when dealing with media such 
as newspapers, people do not do extensive process- 
ing, and yet are able to extract the vast majority of 
information of interest. That is the process we are 
attempting to model here. We do not claim that it 
represents the only level of processing a person can 
use, but we do believe it is a very important and 
widely used level of understanding. 
(There is an aside that is worth making at this 
point. We have talked over the years about how 
expectations drive various parts of the understanding 
process (Riesbeck, 1975 and Cullingford, 1978 for 
example). The contrast here is between expectation- 
based processes and interest-driven processes. Obvi- 
ously the most powerful and important mechanisms 
available to an understander are both expectation 
and interest driven at the same time.) 
Reasons for completely processing a given word 
occur at all levels of the system. Some of these are: 
parser expectations: if the parser expects a certain 
kind of word, the satisfaction of that expectation 
can be taken as an extremely important force in the 
parser. Thus, a parser might function best that ex- 
pected certain syntactic or conceptual types to the 
extent that it ignored everything else until it found 
them. This is again a violation of the idea of left to 
right parsing since a parser might not become inter- 
ested in something until it had already passed it, 
ignored it, and then seen an item that caused expec- 
tations to be raised that could only be satisfied by 
checking backwards. 
syntax: main nouns in a noun phrase can cause a 
processor to try to gather up its modifiers for which 
there is a need or interest. Certain function words 
cause words to be paid attention to if their interest 
value has been predicted. Thus, 'to' is noticed to the 
extent that it can focus attention on the following 
head noun if it has already been determined that a 
location is expected and desired. 
interest values: how does the parser decide what it 
wants to pursue? Obviously we need a fully inte- 
grated system where the parser and memory talk 
during the parsing of a sentence. Without such inte- 
gration, there would be no overriding reasons for 
noticing one thing and not another. It is the role of 
episodic memory and world knowledge to inform the 
parser of what to pay attention to. Interest values 
are stored in memory as part of the knowledge asso- 
ciated with concepts. Certain concepts are nearly 
always interesting, others are interesting in certain 
circumstances. More importantly, certain things are 
interesting on the basis of what has preceded them - 
interestingness is a dynamic property. Thus, the 
object of a shooting might be expected to be more 
interesting if the shooting took place in an embassy 
as opposed to a generally low-interest location such 
as a bar. (But of course, contexts can be created 
where bars can be very important. This is why it is 
necessary to have a dynamic memory available as 
opposed to just a dictionary.) 
top level expectations: if we are reading about an 
event that fits into a high level knowledge structure 
such as a script or a plan, predictions from that 
script or plan can focus interest during the process- 
ing of a sentence. Thus, we can know that the target 
of an assassination and the identity of the assassin 
are of critical importance in reading a story about an 
assassination and we can thus focus in on those 
items as top-down predictions during parsing. 
To see how all this is used consider the sentence 
form "X went to visit Y": Memory is accessed to 
see if X is interesting because it is a main noun and 
because it is a person. When no information is 
found, the processing should be faster than when 
information is found. Thus, when X is 'John' or 
'Sam' we proceed quickly. If X were 'Henry 
Kissinger' or 'your mother' we would presumably 
proceed more slowly because more expectations 
about their behavior that are of interest would be 
found. 
'Went' is an item that urges us to continue proc- 
essing since it has no specific meaning in isolation. 
(That is, we could have 'went crazy', 'went fishing', 
'went to Boston', and we can't do anything until we 
see the next words. The theory here is why specu- 
late at all, just go on.) 'Visit' is a word that calls up 
a script ($VISIT) if the object of the visit is an 
equal or a family member. But other scripts can be 
called up by the word visit that are distinct from 
$VISIT. If the object of visit is 'museum' or 
'Congress' we would get quite different scripts or 
even no available script at all. (What script does 
'went to visit a mortuary' bring up?) Obviously, the 
problem here is that 'visit' is also almost totally ig- 
nored since it too means very little. Its real purpose 
is to get us interested in the object that follows 
next. That is, we don't really start processing this 
sentence deeply until we see what Y is. Then, if Y 
meets certain criteria we instantiate $VISIT. If Y is 
a member of the opposite sex, we have an ambigu- 
ous sentence in terms of script (and thus process- 
ing). In that case, either $VISIT or the RO- 
MANCE script would be applicable, and we will 
now want to figure out which. 
Notice that while most of the examples we have 
presented, and those we will present, describe sto- 
ries in terms of scripts, there is no reason our proc- 
essing ideas could not be used to handle stories bet- 
ter represented by other knowledge structures, such 
as plans and goals (Schank and Abelson, 1977, Wil- 
ensky, 1978). In fact, we believe they will apply 
generally to whatever is represented in memory. 
Scripts are prominent in our early development of 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 17 
J 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
our parsers, because the domain we are concentrat- 
ing on, newspaper stories, tends to involve many 
stereotypical situations. However, we expect to ex- 
tend this work to handle stories requiring different 
types of representations. 
4. An Example 
The following is a sentence taken from a front 
page story in the New York Times: 
An Arabic speaking gunman shot his way into 
the Iraqi Embassy here (Paris) yesterday 
morning, held hostages through most of the 
day before surrendering to French policemen 
and then was shot by Iraqi security officials as 
he was led away by the French officers. 
We will now examine this sentence word by word 
and consider the kind of processing we desire in an 
integrated understanding scheme. The program we 
will describe later does such processing. Our model 
will skip the uninteresting parts and build up its 
representation when necessary, attempting to be 
completely finished with each sub-part at the right 
time. That is, we desire that the model we propose 
be finished processing only slightly after the input 
has been received - just as a person would do. 
One important point here is that although we will 
discuss this sentence in a left-to-right word-by-word 
fashion, there is no real reason to believe that hu- 
man understanding goes one word at a time. Actual- 
ly, words enter in chunks, both visually (in reading) 
and aurally (in speech). We can thus process the 
same way. Thus, for example, the next word to be 
read is available for any word under consideration. 
Such an assumption can simplify the problem of 
disambiguation. 
***************************** 
An Arabic speaking gunman... 
AN is a word that need only be saved initially. This 
means that it is looked up in the dictionary, and 
what is found there are instructions to go to the 
next word and place AN in some form of short term 
memory (STM) to be examined later. 
ARABIC is listed in the dictionary as a word that is 
skippable when it has been preceded by a skippable 
word, so it is skipped and placed in STM. (That 
ARABIC is skippable has already been determined 
and is simply looked up. The procedure for deter- 
mining what can be skipped is obviously one of the 
interesting problems in the issue of the development 
of language ability. (See Schank and Selfridge, 1977, 
for a discussion of these issues.) In general, adjec- 
tives can be skipped, though not all can be. In par- 
ticular, 'Russian' could not be skipped because it 
can also be an actor. Also, adjectives designated as 
interesting may not be skipped, i. e. 'disgusting', 
'murderous', 'lecherous', etc.) 
SPEAKING is also skippable as long as no potential 
actors have been so far encountered. A search for 
ACTORs in STM finds none, so this word is also 
skipped. 
GUNMAN is marked as an ACTOR, as a NOUN, 
and as a HIGH INTEREST ACTOR. The fact that 
we have a HIGH INTEREST word causes us to 
create top-down requests to fill in certain informa- 
tion, in particular we now want to know the answers 
to the following questions: 
WHO is he? .... causes us to gather up stacked ad- 
jectives (e.g. ARABIC) and add them to the 
memory token for this GUNMAN 
WHAT did he do? .... this is answered by an item 
found on the token for GUNMAN, namely 
SHOOT. Thus, an inference that the gun- 
man shot or will shoot somebody is made 
here before anything else comes in as input 
WHOM did he shoot? .... causes us to be interested 
in the syntactic object of the verb which we 
assume will be SHOOT 
WHY did he shoot? .... causes us to look for a rea- 
son 
WHERE did this happen? .... causes us to look for 
a location 
WHAT SCRIPTS might this instantiate? .... GUN- 
MAN can itself cause a script to be instanti- 
ated. Prime candidates are $ROBBERY, 
$TERRORISM and $KIDNAP. We can now 
look for confirmation in the rest of the sen- 
tence. 
The formulation of the above questions (as re- 
quests, see Riesbeck, 1975) now guides the parsing 
of the rest of this sentence. Here it is important to 
point out that much more than just parsing is being 
guided at this point by these requests. This informa- 
tion is what we are interested in as understanders. 
We are actually performing the entire process of 
understanding this story These requests relate to 
matters of parsing and inference and scripts applica- 
tion and goal pursuit as well. 
********************************** 
shot his way into the Iraqi Embassy... 
SHOT is encountered and immediately is found to 
satisfy an expectation that was derived from GUN- 
MAN. Satisfying an expectation of this sort is the 
way that conceptual structures are built and we now 
build the first one, namely a SHOOT action with the 
gunman token as actor and an unfilled final direc- 
tion for the shooting. This unfilled slot is marked as 
the same one that satisfies the answer to the WHO 
question asked before and the parser now is inter- 
18 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
ested in satisfying that request by looking for the 
next main noun in the next noun phrase. 
HIS is skipped and held in STM as before. 
WAY does not satisfy the expectation to fill the 
empty slot. WAY is also listed as both skippable 
and pointing to a direction or location. A request is 
set up for the location and we attempt to skip until 
we find it. 
INTO dictionary entry says to keep on going and it 
is skipped. 
THE is saved and skipped. 
IRAQI is saved and skipped. 
EMBASSY is found to be a location and is set up as 
the location of the SHOOT event. Furthermore, 
EMBASSY is marked as interesting and a place of 
political significance. This latter piece of information 
satisfies the request for instantiating the 
$TERRORISM script that we had predicted (among 
others) from GUNMAN. Since EMBASSY is inter- 
esting, its requests are activated. One of these is for 
a country whose EMBASSY it is. IRAQI is thus 
found in STM as filling this request and is picked 
up. 
Setting up $TERRORISM causes us to lose inter- 
est in the representation of the sentence as such and 
focuses us on setting up and filling requests from 
that script for the representation for the entire story. 
Thus, we now expect answers to the following re- 
quests: 
Were HOSTAGES taken? 
What demands were made? (money; free political 
prisoners?) 
Was any damage done? 
What measures to counteract the terrorist were 
made? 
(return fire; arrest; free hostages?) 
*********************** 
here yesterday morning... 
*********************** 
HERE always refers to the dateline location in a 
news story. It adds this location to the story repre- 
sentation. 
YESTERDAY is found to be a time word and is 
thus added to the time slot of the event. 
MORNING is also handled in this manner. 
************************************* 
held hostages through most of the day .... 
************************************* 
HELD is skipped since it matches none of the re- 
quests. It matches none of them because the infor- 
mation found about HELD in the part of the dic- 
tionary we look at at this point is just that it is a 
verb. No verbs were predicted so we skip it. What 
could have changed this would have been some in- 
terest marking under HELD or other item of signifi- 
cance. Note that HELD is a highly ambiguous word 
that previously might have caused us to make a 
great many predictions and look for evidence of 
what sense was intended. With an integrated under- 
standing system we need not do that at all. The rea- 
son for this can be seen in what happens in subse- 
quent processing of this phrase. 
HOSTAGES is immediately found to satisfy an ex- 
tant request. The TAKE HOSTAGES scene of 
$TERRORISM is instantiated. At this point a check 
is made on the stacked verb to see if doing this is 
okay. If the stacked verb were 'shot', for example, 
this instantiation would not work. HELD is found to 
be precisely the kind of word that fits here. The 
important point is that the meaning of HELD never 
had to be determined in isolation, which is nice be- 
cause words like HELD really do not have any par- 
ticular meaning. . Its meaning is derived from its con- 
nection to HOSTAGES, and HOSTAGES is under- 
stood through $TERRORISM. Thus integrated un- 
derstanding plus "save and skip" parsing facilitates 
processing tremendously. 
THROUGHOUT is found to point to either a time 
or place, so a request is made for a time or place 
word. However, at this point our understanding sys- 
tem knows what it is interested in. In particular, 
satisfying the requests that are still active is very 
important because they are death-related requests 
(see Schank, 1978). Thus we virtually ignore the 
rest of this phrase due to lack of interest. 
MOST is saved and skipped. 
OF is skipped. 
THE is saved and skipped. 
DAY is recognized and ignored. It also satisfies the 
low level request for a time word and this informa- 
tion is added to what we know about time to be 
used later if we ever get interested in what we have 
now decided is uninteresting. 
before surrendering to French policemen... 
BEFORE is a time ordering word that prepares us 
to set up a new event and mark its time relative to 
the preceding event. 
SURRENDERING is a word that is both marked as 
of high interest and as part of a number of scripts 
including $TERRORISM. The surrender scene of 
$TERRORISM is instantiated and requests are fired 
off concerning the reasons for this action, his cap- 
tors etc. Certain words are marked as indicating 
which of these might follow. Thus, 'because' marks 
off reasons, and 'to' marks off captors. 
TO tells us that captors is coming. 
FRENCH is held in STM. 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 19 
Roger C. Schenk, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
POLICEMEN is marked as a noun that can be an 
ACTOR, so STM is consulted to gather up its rele- 
vant components. POLICEMAN also is a possible 
captor (because it is both a human and an institu- 
tion, either of which would do), so it satisfies two 
extant requests. 
and then was shot by Iraqi security officials .... ****************************************** 
AND says whenever an event has just ended, a new 
event may be coming. 
THEN orders the time of the event. 
WAS specifies that the actor stored in STM is the 
conceptual object of the new event. This sets up 
requests for the actor and the action. 
SHOT is found to be interesting and is treated simi- 
larly to the way that GUNMAN was, except that we 
do not expect the things that were particular to 
GUNMAN as opposed to the action he was per- 
forming. Thus we have: 
WHOM did he shoot? .... ARAB GUNMAN 
WHO shot? .... not answered 
WHY did he shoot? .... not answered 
WHERE did this happen? .... already known 
WHAT SCRIPTS does this instantiate? .... SHOOT 
can also cause a script to be instantiated. 
Prime candidates are $ROBBERY, 
$TERRORISM and $KIDNAP, ordinarily. 
But we are in a context set up by 
$TERRORISM. None of the above are nor- 
mal continuations of $TERRORISM. This 
causes us to look for plans and goals. 
WHAT were the RESULTS of this action? -- A 
request is set up to find the results. If this 
request is not satisfied the usual results of 
this action are inferred. In this case death 
for the object. 
Since SHOT is interesting, we need to explain it. 
No scripts are available here, so we need to ask who 
would want to kill the GUNMAN and why. These 
requests are added to the active requests. 
BY tells us the actor is to follow. 
IRAQI is stacked and skipped. 
SECURITY is stacked and skipped. 
OFFICIALS is used to end the processing of the 
noun group. It satisfies the requests for WHO did 
the shooting, and, as we now have an actor, we ask 
about why he would kill a TERRORIST. This causes 
us to examine the themes we have for why TER- 
RORISTS might be killed after capture. At this 
point we might make the connection between IRA- 
QI SECURITY OFFICIALS and IRAQI EMBASSY, 
but that does not lead to an explanation. This caus- 
es us to be surprised by this event. We seek to ex- 
plain it by postulating a REVENGE or SHUT HIM 
UP type theme, but we are certainly not sure of it. 
as he was led away by French officers. 
*********************************** 
We are basically done now as no further requests 
need to be satisfied immediately. (We know this 
after we have seen a period and found no new re- 
quests.) We are still interested in the goals of each 
of the actors, however, so WHY requests are still 
alive. 
AS is known to be a time co-occurrence word. 
Since we are not interested in anything that occur- 
red at the same time unless it is itself interesting, we 
can now skip ahead looking for actions or actors 
that are interesting. AS can also indicate causality, 
but in that case the semantic predictions set up ear- 
lier will find the cause. 
HE is skipped. 
WAS is skipped. 
LED is uninteresting and is recognized and then 
skipped. 
AWAY is skipped. 
BY is skipped. 
THE is skipped. 
FRENCH is skipped. 
OFFICERS is skipped because there are no requests 
asking for it. 
The period tells us we are done. 
The final representation for this sentence is: 
$TERRORISM 
ACTOR - Arab gunman 
PLACE - Paris, Iraqi Embassy 
SCENES 
$HOSTAGES - some 
$CAPTURE 
ACTOR - French policemen 
OBJECT - Arab gunman 
PLACE - Paris, Iraqi Embassy 
UNEXPECTED RESULT: 
ACTOR - Iraqi officers 
ACTION - SHOOT 
OBJECT - Arab gunman 
RESULT 
ACTOR - Arab gunman 
STATE - dead 
5. Processing in Integrated Partial Parsing 
We have written a program which implements the 
theory of parsing illustrated above - an Integrated 
Partial Parser (hereafter IPP). In this section we 
shall look at how this parser works. It was written 
to handle a limited class of stories, namely newspa- 
per stories about terrorism and related areas. We 
have not tried to address all the issues involved in 
20 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
parsing. Rather we have concentrated on the areas 
which are crucial to IPP. One obvious problem we 
have not addressed is words with multiple senses. 
Fortunately, in the class of stories we are process- 
ing, most words, especially the interesting ones, have 
one strongly preferred sense. IPP has successfully 
processed over 200 stories taken directly from vari- 
ous newspapers. Many of these were processed sight 
unseen. IPP current has a vocabulary of over 2000 
words. The parser is written in LISP, and runs on a 
DEC System 20/50. 
The program's limitations center around its vo- 
cabulary and knowledge of the world. 2000 words, a 
sizable vocabulary for a typical AI program, is still a 
bit too small, even for stories about terrorism. We 
are currently expanding the vocabulary. The pro- 
gram is also limited to understanding stories for 
which it has appropriate world knowledge. We have 
concentrated on stories that are script-based, but as 
mentioned earlier, we believe the general techniques 
of IPP will extend to other forms of knowledge. 
IPP's ability can be increased both by adding more 
script-type information, similar to that it already 
has, and by considering these other types of world 
knowledge. 
Another limitation IPP has is that it has problems 
with stories which are subtly phrased - those where 
jumping to a conclusion causes problems. But these 
are exactly the kinds of stories people have trouble 
with when they are reading quickly. The projected 
solution for this problem is to give IPP the capabili- 
ty of going back and reading text in a more careful 
mode than it normally does. 
The parsing scheme implemented in IPP is based 
on classifying the words in the dictionary in terms of 
what the parser should do with each word as it reads 
it. Thus, labels such as noun, verb, etc. only make 
sense in a parser if they cause different processing 
dependent on seeing such classifications. 
It is very well to say, as we have, that a given 
word should be skipped or saved or whatever. We 
must make these determinations beforehand, howev- 
er. Thus the key issues in the realization of this par- 
ser are, first, the establishment of a set of categories 
for the words in the dictionary that will be useful in 
such a scheme; and, second, a procedure for deter- 
mining what category a given word fits into. As we 
will see shortly, the category a word is assigned to 
may be domain dependent. 
Looking back at the example in the last section, 
we can see that there are basically three different 
things that can be done with a word when it is read. 
It can be skipped, it can be saved and then skipped, 
or it can be completely processed immediately. 
The first possibility is that it may simply be skip- 
ped. There are many words which have no signifi- 
cant conceptual content for normal reading. Exam- 
ples from the story in the last section include the 
Words 'most', 'way', and 'held'. 
The second possibility that we can see from the 
example is that a word may be saved in some kind 
of short term memory and then skipped. Words for 
which this processing strategy seems appropriate 
have some functional purpose or significant concep- 
tual content of a rather dull and uninteresting sort. 
Nevertheless, we cannot simply ignore them, be- 
cause their meanings may be important in elaborat- 
ing our knowledge of the events or things that we 
are interested in. For example, they may be used to 
fill roles in the conceptual structures representing 
interesting events. They may also never be used 
again. Many of the words in our example are proc- 
essed this way. Examples include the words 
'Arabic', 'Iraqi', and 'his', as well as all articles. 
Two things can happen with these words. Either 
their meaning does help elaborate something inter- 
esting, in which case that meaning will be incorpo- 
rated in the representation, or it doesn't. For exam- 
ple, the meaning of the word 'French' in the phrase 
(1) before surrendering to French police 
is incorporated into the representation because we 
are interested in whom the terrorist surrendered to, 
i.e. 'police'. On the other hand, the meaning of the 
word 'French' in the phrase 
(2) as he was led away by the French officers 
is not incorporated into the meaning representation 
because we never become interested in 'officers'. 
Words with some conceptual content will often 
also have some associated processing information, in 
the form of expectations, which can help to elabo- 
rate on their own meaning. Many of the words 
which are processed by the "save and skip" strategy 
are objects of these sorts of expectations. For ex- 
ample, it seems quite plausible that the word 
'embassy' has an expectation which looks for the 
name of the country which the embassy represents, 
and that the words 'police', 'officers', and 'officials' 
have expectations for the name of the governmental 
authority in whose name they operate. But if a 
word is subject to the "save and skip" strategy, 
these expectations should not be applied until we 
know that the concept associated with the word 
actually elaborates on our knowledge of something 
interesting. If it turns out that we don't care about 
the concept, we don't want to have done unneces- 
sary processing. Let's compare our processing of 
'police' in phrase (1) above with our processing of 
'officers' in phrase (2). Since it turns out that the 
concept of police in the first case adds to our 
knowledge of an interesting event, it seems plausible 
that the expectation that the word 'police' has for 
the authority governing the police would be used. 
In the second case, since the concept of 'officers' 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 21 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
does not add to our knowledge of anything interest- 
ing, there is simply no point in applying any similar 
rule. 
The third possible processing strategy we can 
apply to a word is to process it immediately, i.e. pay 
attention to its meaning and the expectations it gen- 
erates. This is the strategy that we apply when the 
word has a significant and interesting conceptual 
content. It is these concepts and their associated 
expectations that drive the analysis. Examples from 
the story of the last section include the words 
'gunman', 'shot', and 'hostages'. The expectations 
which these words generate include the same kind of 
simple elaborative, or "slot-filling", expectations 
associated with some of the words for which a "skip 
and save" strategy is appropriate. For example, it is 
quite plausible that one expectation generated by 
the word 'gunman' looks for the nationality or polit- 
ical affiliation of the gunman. 
These words can also generate expectations 
which operate at a much higher level. For example, 
when we read the word 'gunman', we expect to read 
that he may have performed the action of shooting a 
weapon. We also expect the events associated with 
several possible scripts, including $ROBBERY and 
$TERRORISM. These expectations operate in a 
manner somewhat akin to script application (see 
Cullingford, 1978), in that they serve to recognize 
events, and so recognize that they are sensible in the 
given context. So, as described in the example of 
the last section, once we know that the gunman is 
quite likely a terrorist, we expect that he may hold 
hostages, that he may shoot or kill some people, and 
that he may make demands. We also know that 
there are only a small number of possible outcomes 
of the episode: the terrorist might be captured, he 
might surrender, he might be killed, or he might 
escape. These high level expectations help us decide 
what is important in the text in a very top-down 
way. The analysis process depends crucially on this. 
But its flexibility also depends on its ability to pur- 
sue questions about interesting things and events, 
even if they were not anticipated. 
The expectations used by IPP are implemented in 
the form of requests (see Riesbeek, 1975). A re- 
quest is a form of production, or test-action pair. If 
the test of an active request is checked and found to 
be true, then the corresponding sets of actions are 
performed. The list of requests is ordered so that 
when the active requests are considered, the most 
recently activated are considered first, since they 
represent newer, and so probably better, expecta- 
tions. 
While in theory the tests and actions which re- 
quests perform could be arbitrary, in our system we 
have found that only a restricted set is necessary. 
Requests may do the following: 
build new conceptual structures -- usually a 
given request will only build one such struc- 
ture; 
fill a slot in some conceptual structure with 
some other conceptual structure -- for exam- 
ple, filling the ACTOR slot of $SHOOT with 
the token for the gunman; 
activate other requests -- these will often be 
requests trying to fill slots in the structure 
built by the activating request; they can also 
be expectations for possible actions, states, or 
more complicated episodes which may follow; 
de-activate requests -- requests are able to 
deactivate requests, including themselves, 
when they are no longer appropriate. 
There are three types of tests which requests per- 
form: 
checks for specific lexical items -- for exam- 
ple, function words tend to be specific to a 
given construction; so in the phrase 
"surrender to French police", the requests 
associated with 'surrender' (or $SUR- 
RENDER), can look for the occurrence of the 
word :to' to precede the authority to whom 
the surrender is taking place; 
checks for lexical items satisfying some prop- 
erty -- for example, words which activate a 
specific script; 
look for tokens or events of a specified type 
-- this might be as simple as matching a par- 
ticular structure; or it may involve use of se- 
mantic tests such as 'human' or 'authority'. 
The fact that requests can look for specific lexical 
items is very important in reducing processing time. 
This savings is realized both by requests looking for 
function words to fill slots (such as 'to' or 'by'), and 
by requests which look for more substantial events. 
Often a word may create expectations which look 
for specific words which indicate what script is ap- 
plicable. As an example, gunman creates expecta- 
tions which look for the terrorist, hijack, and rob- 
bery scripts. The request looking for the hijack 
script may include tests for specific words (or phras- 
es), such as 'diverted', 'hijack', 'took over', all of 
which indicate the hijack script. Requests will nor- 
mally have checks at the conceptual level as well. 
The request activated by 'gunman' which checks for 
the terrorist script looks at the location of the ac- 
tion. If that location is the location of a political 
entity, such as an embassy or the office of some 
political organization, that is a good clue that the 
terrorist script may be relevant. A sample request is 
shown on the next page. 
Within the broad categories of words that are 
processed immediately, and words that are saved 
and skipped, there are subcategories that help to 
22 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
Samp~ Request 
~ FIND-$HIJACK instantiates the hijacking script by noticing an 
appropriate word or concept, builds a hijacking event, 
~ and sets off several new requests, looking for scenes 
~ and other actions. (Created by GUNMAN.) 
(DEF-REQ FIND-$HIJACK 
TEST (HIJACK-INSTANTIATOR *NEW-ITEM*) 
ACTION (REQ-EVENT 8 (SCRIPT SHIJACK 
ACTOR NIL 
DEMANDS NIL 
FROM NIL 
DESTINATION NIL 
TO NIL 
PASSENGERS NIL 
VEHICLE NIL) 
~ Test looks for words which 
~ indicate the hijack script 
~ Action builds an event for 
~ the hijack script of 
~ interest 8, with the slots 
shown here. It fills in 
the actor slot with the 
last actor in *ACTOR-STACK* 
((ACTOR . (TOP-OF *ACTOR-STACK*))) 
(REDUNDANT-HIJACK-WORDS ~ These new requests are 
FIND-HIJACK-DESTINATION ~ activated. 
FIND-HIJACK-VEHICLE 
FIND-HIJACK-PASSENGERS 
FIND-HIJACK-EVENTS 
SURRENDER-SCENE 
RECOGNIZE-DEMANDS 
RECOGNIZE-COUNTER-MEASURES\] 
decide what to do with a given word. There are two 
considerations that affect a word's classification. 
The first is how a given word modifies the repre- 
sentation we are building; the second is the kind of 
expectations that a word sets up. The classification 
scheme is based on these two considerations. We 
will now describe each class of words, and how IPP 
processes them. For each class, a sample dictionary 
entry is shown. 
A - Words that are immediately processed 
Within a theory of integrated parsing, words are 
best classified according to the type of conceptual 
structures that they build. That is, the most impor- 
tant role that a word plays, in this conception of 
processing, is not its syntactic role such as noun or 
verb, or even its conceptual role, such as actor or 
action. The most significant thing about a word from 
this point of view is how it affects the processing 
within the integrated understanding process. 
In the representation given above for the Arab 
gunman sentence there are two different kinds of 
items. There are the events involved - the terrorism 
script, the capture scene, the gunman being shot, 
and so forth; there are also the individual concepts 
that play roles - the gunman who fills the ACTOR 
slot of the terrorism script, or the Iraqi embassy, 
which fills the LOCATION slot of this script, for 
example. These role fillers we shall refer to as to- 
kens. With the distinction between tokens and 
events in mind, we can look at a classification of 
words. 
A1 - Event Builders 
One class of words are those that build event 
structures. We call these Event Builders (EB's) This 
class of words includes many verbs, and a number 
of nouns, such as 'killing', 'riot', and 'hijacker'. All 
EB's have an associated interestingness. This helps 
determine whether an event is significant enough to 
be included in the final representation - whether it is 
interesting enough to cause us to construe it as a 
central event in the representation, and whether it is 
important enough that we should spend valuable 
processing time attempting to fill its open slots. All 
EB's also have an associated set of expectations that 
help to guide the rest of the parse. These expecta- 
tions vary from explicit requests to place subsequent 
items in specific slots, to general expectations about 
events that are likely to occur eventually (such as 
the scenes of a script). 
EB's are further subdivided according to the type 
of event they build. Many very common words, such 
as 'give', 'went', and 'ate', build simple (and not 
intrinsically very interesting) events. These events 
are the kind that we have always been able to repre- 
sent very easily in Conceptual Dependency (Schank, 
1972, 1975). In our recent work on higher level 
knowledge structures, we have found that the kinds 
of representations that are most significant are those 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 23 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
that relate to scripts, plans, and goals (see Schank 
and Abelson, 1977). Consequently, those EB's that 
build simple Conceptual Dependency structures, are 
precisely those that need the least processing be- 
cause they are the least interesting. They constitute 
a special class of EB's then, (CDEB's), that rarely 
require us to spend much time on them. They have 
rather simple expectations, generally to fill in their 
ACTOR, OBJECT, TO, FROM, and INSTRU- 
MENT slots. In order for us to attempt to find the 
information that fills these expectations, some more 
. interesting event must expect them, or there must be 
an interesting actor whom we expect to be involved 
with the action. 
Other kinds of EB's are script builders ($EB's) 
and scene builders (SEB's). Both of these types can 
have rather more involved requests, often suggesting 
events that might occur. The only real'difference 
between $EB's (words such as 'hijacked', 'kidnap') 
and SEB's ('surrendered', 'convicted') is that from 
SEB's we try to infer a script, since scenes cannot 
occur in isolation, and from $EB's we create expec- 
tations for the scenes of the script. 
Other knowledge structures used to understand 
stories, such as plans, goals, and themes also have 
associated EB's (that is, words that build these 
structures directly) but the EB's described so far 
are sufficient for a large class of newspaper stories. 
(Higher level knowledge structures are generally not 
stated directly by any particular word. Rather, the 
presence of such structures usually must be detected 
by inference.) 
When an EB is read, an empty event structure is 
built from a template in the dictionary. IPP then 
checks to see if any requests are looking for this 
event. Expectations created by the context of the 
story frequently explain an event with little further 
effort. If there are no relevant expectations, the 
event's interest value, listed in the dictionary, is 
checked. If the event has little interest, processing 
moves to the next word. If the event has significant 
interest, the expectations listed in the word's dic- 
tionary entry are instantiated, with a pointer to the 
new event structure. 
IPP keeps track of a story's main event. It 
checks to see if a new event is more interesting than 
its current main event. If an interesting event less 
interesting than the current main event is created, 
and it does not fulfill an expectation, then it is saved 
as an unexplained event, indicating IPP should look 
for an explanation. 
A2 - Token Makers 
Many words, including most nouns, such as 
'gunman' and 'embassy', contribute to the process of 
understanding by filling open slots in event struc- 
tures. We call this class of words Token Makers 
(TM's). These words cause a token to be built. If 
the word is interesting, or an interesting modifier 
has been saved in short term memory (and only in 
these cases), then the words which modify the token 
are retrieved from short term memory. The tokens 
built are frequently objects looked for by expecta- 
tions made during the processing of previous words 
in the sentence. 
The class of TM's can be subdivided in two 
ways. There are several different types of tokens 
which can be built, such as actor tokens, place to- 
kens, organization tokens, vehicle tokens and time 
tokens. The type of token built is one factor in de- 
termining whether the new token satisfies an expec- 
tation made earlier. 
The other subdivision of TM's concerns the ef- 
fects that TM has on subsequent processing. This 
Sumps Dmtionary Entry (A1) 
WORD-DEF OCCUPIED 
INTEREST 5 
TYPE EB 
SUBCLASS 
TEMPLATE 
FILL 
REQS 
SEB 
SCRIPT SDEMONSTRATE N OCCUPIED builds a structure 
ACTOR NIL ~ specifying the demonstrate 
OBJECT NIL - script with an occupy 
DEMANDS NIL ~ scene 
METHOD (SCENE $OCCUPY 
ACTOR NIL 
LOCATION NIL)) 
((ACTOR) (TOP-OF *ACTOR-STACK*)) - ACTOR slots 
((METHOD ACTOR) (TOP-OF *ACTOR-STACK*))) ~ are filled. 
FIND-DEMON-OBJECT ~ Expectation we might see who is being 
demonstrated against. 
FIND-OCCUPY-LOC - Expectation we might see the site of 
the demonstration. 
RECOGNIZE-DEMANDS\] ~ Expectation we might see demands. 
24 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
Sample Dictmna~ Ent~ (A2) 
(WORD-DEF GUNMAN 
INTEREST 5 
TYPE TM 
SUBCLASS ACTOR 
MEMORY T 
REQS (CONFIRM-SHOOT 
FIND-WHY-SHOOT 
(FIND-$TERRORISM 
FIND-$ROBBERY 
FIND-$KIDNAP 
FIND-$HIJACK\] 
~ GUNMAN is an ITM 
Expectation we might see a shooting. 
Expectation we might see why the 
gunman would shoot someone. 
~ Set of expectations which specify 
~ scripts we are likely to see. 
~ If one is satisfied, the others 
~ are deactivated. 
division is based on how interesting the TM is. In- 
teresting TM's (ITM's) generate expectations as to 
what we might see next in the sentence. Thus 
'gunman', an ITM, generates expectations for shoot- 
ing, hijacking, and robbery events, for example. 
ITM's that fill the actor role in an event naturally 
generate expectations that more information about 
these people will be forthcoming. For example, 
'gunman' activates requests looking for feasible 
scripts. 
TM's that are not interesting, and hence do not 
generate any expectations, can be placed into two 
classes, normal (NTM's) and empty (ETM's). 
NTM's can easily be associated with objects already 
in memory, even though they are not interesting. 
Examples of NTM's are 'airport', 'Vermont', and 
'officials'. The tokens built by NTM's can be used 
to fill slots in the representation. ETM's, on the 
other hand, are words which are so indistinct in 
memory that it is virtually meaningless to include 
them in the final representation of the sentence. 
Words such as 'people', 'place', and 'someone' fall 
into this class. These words build tokens which can 
deactivate expectations, but they are not added into 
the final representation. If there is no expectation 
for the token built, and it is not interesting by itself, 
it is ignored in our parsing scheme, since there is 
little reason to remember it. 
B - Words that are saved and skipped 
Many words need no processing when they are 
first read. They are simply saved in short term 
memory and their processing completed later, if 
necessary. There are two important points to recog- 
nize about save and skip words. First, the fact that 
we save a word does not commit us to doing any 
further processing of it. Most save and skip words 
are not very interesting, and unless a subsequent 
interesting word requests that saved words be con- 
sidered, save and skip words can easily require no 
processing other than being saved. Presumably the 
process of saving a word is very easy, so that save 
and skip words often consume very little processing 
time. An important point about save and skip words 
is that domain and context are important in deter- 
mining which words are save and skippable, and 
which are totally skippable. So for example, a word 
like 'tall', is totally skippable in most domains (such 
as stories in most sections of a newspaper), but 
when reading a sports story, it may become a save 
and skip word, since height can be salient in certain 
situations. 
The class of save and skippable words can be 
subdivided into several classes, based on what we do 
with the word, if we do decide to process it further. 
(Remember - there is a good chance no further 
processing will be done.) 
B1 - Token Refiners 
One class of save and skip words, token refiners 
(TR's), add information to the tokens built by TM's. 
Most of the words which commonly appear in noun 
phrases, including many adjectives, are TR's in do- 
mains in which they cannot be skipped entirely. 
Above, 'Arabic' is a TR which refines the actor to- 
ken built for the gunman, by marking it "nationality: 
Arabic". The processing for all TR's begins in the 
same way. Each TR is stored temporarily, until the 
TM it modifies is found, at which point it may be 
retrieved and processed further, in a manner de- 
pendent on the TR type. (If the TM proves to be 
uninteresting, no further processing will be done.) 
The class of TR's can be subdivided three ways, 
based on how they alter the tokens they modify. A 
large class of TR's simply add a property to a token. 
These TR's, which will be referred to as simple TR's 
(STR's) include common adjectives, such as 'red', 
'tall', and 'Arabic', in the cases where they are not 
just skippable. Words like 'early', or 'late', fall into 
this class, usually modifying time TM's. 
Other TR's modify properties added to a token 
by another modifier. For instance, in the phrase 
"about 20 gunmen," 20 would add to the token for 
gunmen, "NUMBER 20," and 'about' would alter 
this to "NUMBER (APPROX 20)." Words in this 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 25 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
class of TR's are called TR modifiers, or TRM's. It 
is not clear how often words in this class are not 
simply skippable. It seems likely that most of these 
words tend to get ignored nearly all of the time, but 
sometimes they must be saved and skipped. 
The third class of TR's are names (TRN's). They 
simply add to the token they modify the information 
about the token's name. So in "Kennedy Interna- 
tional Airport," 'Kennedy' adds to the airport token 
the fact that its name is Kennedy. TRN's differ in 
processing from STR's only in that they cannot be 
modified by TR Modifiers. 
One aspect of processing which is common to all 
types of TR's is that their dictionary entries can 
indicate they should make the token they modify 
more interesting. So, "Arabic gunman," is more 
interesting than 'gunman', due to the inherent inter- 
est of the TR 'Arabic'. 
Notice that "save and skip" processing would 
make it very easy to handle TR's whose meaning is 
dependent upon the words they modify, since the 
actual definition of the TR is not processed until the 
TM is known. It also simplifies cases where the TM 
actively looks for specific types of words which 
might modify it. 
Sample Dictionary Entry (B1) 
( WORD-DEF ARABIC 
TYPE TR 
SUBCLASS STR 
INTEREST 2 
MEMORY T 
DEF (NATIONALITY . ARABIC\] 
B2 - Event Refiners 
Event refiners (ER) are very similar to TR's, 
except they modify events, not tokens. Typical of 
this class are adverbs such as 'quickly', 'stupidly', 
and other 'ly' words. Other words such as 'here' and 
'away' also fall into this class, since they alter a Slot 
of the event they modify, as in "was shot here," or 
"was led away." Words which might appear to fall 
into this class are even more likely to turn out to be 
skippable than TR's. The 'ly' words just mentioned 
are ER's when they are saved, but in general they 
are very dull words, and get skipped entirely. As 
mentioned above, the determination of whether the 
word must in fact be saved is domain dependent. 
ER's divide into standard ER's (SER's) and ER 
modifiers (ERM's) in a manner similar to STR's and 
TRM's. Processing is similar to that for TR's, except 
it occurs when an event is created, and ER's are 
looked for following the event, as well as those 
which have been saved in STM. 
Sample Dictionary Entry (B2) 
( WORD-DEF AWAY 
TYPE ER 
SUBCLASS SER 
DEF ( TO . NOT-HERE\] 
B3 - Function Words 
There is an important class of words in English 
which have little or no meaning of their own, but 
exist solely to guide processing. These words, known 
as function words (FW's), are quite common, and 
include articles, prepositions, and auxiliary verbs. 
Function words in general cannot be totally skipped, 
but quite often the parsing process never returns to 
them. They must be saved, since if interesting items 
follow they may become important, but by them- 
selves they do not demand processing. 
The role of articles (a, an, the) is to mark the 
beginning of noun phrases, and help indicate which 
Token Refiners go along with which Token Makers. 
When read, they are saved with the TR's. Then, 
when processing a TM, we look back on the words 
just encountered trying to find TR's. If we find an 
article, this search terminates. 
Prepositions (with, to, from ...) have a variety of 
functions in English. Often they precede TM's and 
indicate how the TM should be added to the struc- 
ture being built. In our system, the most frequent 
use for prepositions is an inactive one. An EB will 
often create expectations for a certain preposition, 
with instructions for what to do with the TM follow- 
ing the preposition. Thus 'shot' creates an expecta- 
tion for 'with', and knows that the TM following 
'with' should go into the INSTRUMENT slot of the 
event. 
Auxiliary verbs have a variety of functions, such 
as setting time (did go), or making the event to fol- 
low hypothetical (may go). One of the more impor- 
tant uses of auxiliary verbs is the use of forms of 'to 
be' to make a verb passive. When an event is creat- 
ed by a past participle, IPP checks for such an auxil- 
iary, and if one is present, modifies low-level proc- 
essing appropriately. 
Sample Dictionary Entry (B3) 
( WORD-DEF A 
TYPE FW 
SUBCLASS ART\] 
B4 - Relational Words 
Relational words create a link between two 
events. Processing of all these words tends be the 
same. The word is saved temporarily until a signifi- 
cant event is found. Then the proper link between 
that event and the previous event is made. If the 
26 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
relational word connects uninteresting events in the 
sentence, no additional processing will be done. 
Relational words create two main kinds of links - 
temporal and causal. Words such as 'before', 
'while', and 'after' indicate temporal relations be- 
tween events, and 'because', 'since', and 'therefore' 
indicate causal relations. 
Sample Dictionary Entry (B4) 
( WORD-DEF BEFORE 
TYPE RW 
SUBCLASS TRW 
RELATION AFTER\] 
C - Skippable Words 
A somewhat surprisingly large class of words is 
entirely skippable. When we process them, absolute- 
ly nothing is done. This is presumably one means for 
saving substantial amounts of time during process- 
ing. Words such as 'and', 'who', and 'speaking' (as 
used above) fall into this class. An important topic 
of future study is to discover just what qualifies a 
word as skippable. The larger the skippable class 
becomes, the faster this program will be. It is likely 
that few, if any, words are skippable in all domains, 
for all readers, whatever level they are processing. 
But for a given reader, working in a given domain, 
many words are skippable. 
Also words can be added to the skippable class 
dynamically, even seemingly quite interesting words. 
So if we already know the "hold hostage" script is 
taking place, words like 'terror', 'siege', and 
'gunfire', become skippable, since we have already 
inferred anything they would build. Expectations 
for such words are created which neutralize their 
inherent interest. 
6. Examples of IPP 
The first three examples shown here are comput- 
er runs of IPP on three stories taken from the New 
York Times. 
Yale TOPS-20 Command processor 3(414) 
@DO IPP 
*(PARSE SI) 
Input: 
(AN ARABIC SPEAKING GUNMAN SHOT HIS WAY 
INTO THE IRAQI EMBASSY HERE THIS MORNING 
HELD HOSTAGES THROUGHOUT MOST OF THE DAY 
BEFORE SURRENDERING TO FRENCH POLICEMEN 
AND THEN WAS SHOT BY IRAQI SECURITY 
OFFICIALS AS HE WAS LED AWAY BY FRENCH 
OFFICERS) 
** MAIN EVENT ** 
SCRIPT STERRORISM 
ACTOR ARAB GUNMAN 
PLACE IRAQI EMBASSY 
CITY PARIS 
TIME MORNING 
SCENES 
SCRIPT SHOLD-HOSTAGES 
ACTOR ARAB GUNMAN 
PLACE IRAQI EMBASSY 
SCRIPT $CAPTURE 
ACTOR POLICEMEN 
OBJECT ARAB GUNMAN 
PLACE IRAQI EMBASSY 
AFTER SHOLD-HOSTAGES SCENE 
** UNEXPECTED EVENTS ** 
SCRIPT $SHOOT 
ACTOR IRAQI OFFICIALS 
OBJECT ARAB GUNMAN 
AFTER $CAPTURE SCENE 
RESULT 
STATE DEAD 
ACTOR ARAB GUNMAN 
*(PARSE $2 ) 
Input: 
(A GUNMAN WHO DIVERTED A VERMONT BOUND 
BUS WITH MORE THAN TWENTYFIVE PASSENGERS 
FROM THE BRONX TO KENNEDY INTERNATIONAL 
AIRPORT AND KILLED TWO HOSTAGES 
SURRENDERED ON A RUNWAY LATE LAST NIGHT 
ENDING A DAYLONG SIEGE OF TERROR AND 
GUNFIRE) 
Output: 
** MAIN EVENT ** 
SCRIPT $HIJACK 
ACTOR GUNMAN 
FROM BRONX 
TO AIRPORT 
CARRYING PASSENGERS 
VEHICLE BUS 
SCENES 
SCRIPT SKILL 
ACTOR GUNMAN 
VICTIM HOSTAGES 
SCRIPT $CAPTURE 
OBJECT GUNMAN 
ACTOR POLICE 
TIME NIGHT 
** UNEXPECTED EVENTS ** 
NONE 
Output: *(PARSE $3 ) 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 27 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
Input: 
(ABOUT TWENTY PERSONS OCCUPIED THE OFFICE 
OF AMNESTY-INTERNATIONAL SEEKING BETTER 
JAIL CONDITIONS FOR THREE ALLEGED 
WEST-GERMAN TERRORISTS) 
Output: 
** MAIN EVENT ** 
SCRIPT 
OBJECT 
DEMANDS 
SDEMONSTRATE 
AMNESTY-INTERNATIONAL 
IMPROVED JAIL CONDITIONS FOR 
WEST-GERMAN TERRORISTS 
METHOD 
SCRIPT $OCCUPY 
** UNEXPECTED EVENTS ** 
NONE 
\[PHOTO: terminated Thu 16-Nov-78 8:27AM\] 
The next two examples, the first from the Boston 
G~be and the second from the New York ~mes, 
illustrate how stories with simUar content are proc- 
essed similarly by IPP, despite differences in syntax. 
Yale TOPS-20 Command processor 3A(415) 
@DO IPP 
*(PARSE $4) 
Input: 
(IRISH REPUBLICAN ARMY GUERRILLAS 
AMBUSHED A MILITARY PATROL IN WEST 
BELFAST YESTERDAY KILLING ONE BRITISH 
SOLDIER AND BADLY WOUNDING ANOTHER ARMY 
HEADQUARTERS REPORTED) 
Output: 
** MAIN EVENT ** 
SCRIPT STERRORISM 
ACTOR IRA GUERRILLAS 
PLACE BELFAST 
TIME YESTERDAY 
SCENES 
SCRIPT SKILL 
ACTOR IRA GUERRILLAS 
VICTIM I ENGLISH SOLDIER 
PLACE BELFAST 
SCRIPT SWOUND 
ACTOR IRA GUERRILLAS 
PLACE BELFAST 
** UNEXPECTED EVENTS ** 
NONE 
*(PARSE S5 ) 
Input: 
(GUNMEN BELIEVED TO BE BASQUE GUERRILLAS 
TODAY SHOT AND SERIOUSLY WOUNDED A 
PROVINCIAL SECRETARY OF THE RIGHT-WING 
POPULAR ALLIANCE PARTY POLICE SOURCES 
SAID) 
Output: 
** MAIN EVENT ** 
SCRIPT STERRORISM 
ACTOR BASQUE GUERRILLAS 
TIME TODAY 
SCENES 
SCRIPT $SHOOT 
VICTIM SECRETARY 
ACTOR BASQUE GUERRILLAS 
SCRIPT SWOUND 
ACTOR BASQUE GUERRILLAS 
VICTIM SECRETARY 
EXTENT GREATERTHAN-*NORM* 
** UNEXPECTED EVENTS ** 
NONE 
\[PHOTO: terminated Tue 5-Jun-79 I:08PM\] 
Notice that in $5, the primary designation of the 
actor is given in the participial phrase, "believed to 
be Basque guerrillas," while in $4 "Irish Republican 
Army Guerrillas" is simply the subject of the sen- 
tence. IPP identifies the actors in the same way, just 
as people would normally do. (I.e. the qualifier 
"believed to be" is normally ignored.) Also notice 
that the events described in $4 by "killing" and 
"wounding" are no more difficult for IPP to under- 
stand than those described by "shot" and 
"wounded" in $5. In fact, the processing is virtually 
identical. 
The final two examples illustrate how IPP can 
jump to a conclusion about the representation of a 
story and then drop that representation when it 
finds a more interesting possibility. The first exam- 
ple, $6 is the initial fragment of the full sentence 
processed in the second example, $7, which is from 
the New York Times. 
Yale TOPS-20 Command processor 3A(415) 
@DO IPP 
*( PARSE $6 ) 
Input : 
(A YOUNG JAPANESE GUNMAN BROKE INTO A 
TOKYO BANK TODAY AND KILLED TWO 
POLICEMEN ) 
Output: 
** MAIN EVENT ** 
SCRIPT SROBBERY 
ACTOR JAPANESE GUNMAN 
PLACE TOKYO BANK 
28 American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
TIME TODAY 
SCENES 
SCRIPT SKILL 
ACTOR JAPANESE GUNMAN 
VICTIM 2 POLICEMEN 
PLACE TOKYO BANK 
** UNEXPECTED EVENTS ** 
NONE 
*(PARSE $7) 
Input: 
(A YOUNG JAPANESE GUNMAN BROKE INTO A 
TOKYO BANK TODAY KILLED TWO POLICEMEN 
HELD 36 PERSONS HOSTAGE AND VOWED HE 
WOULD NOT LEAVE UNLESS HE RECEIVED 
$25OOOO) 
Output: 
** MAIN EVENT ** 
SCRIPT STERRORISM 
ACTOR JAPANESE GUNMAN 
DEMANDS $250000 
PLACE TOKYO BANK 
SCENES 
SCRIPT SKILL 
ACTOR JAPANESE GUNMAN 
VICTIM 2 POLICEMEN 
PLACE TOKYO BANK 
SCRIPT $HOLD-HOSTAGES 
HOSTAGES 36 PERSONS 
ACTOR JAPANESE GUNMAN 
** UNEXPECTED EVENTS ** 
NONE 
\[PHOTO: terminated Tue 5-Jun-79 5:20PM\] 
Initially IPP assumed the story to be an instance 
of the ROBBERY script, since 'bank' triggered a 
prediction from 'gunman'. The ROBBERY script has 
completely disappeared in the final representation of 
$7. Once IPP discovered the HOLD HOSTAGES 
script, from which it inferred TERRORISM, it de- 
cided that was much more interesting than ROB- 
BERY, and selected it as its representation. Howev- 
er, it did reincorporate the KILL scene into the new 
representation. 
7. Conclusion 
Careful readers will note that we have used little 
in the way of Conceptual Dependency (Schank, 
1972, 1975) in the final representations that we 
have used as the output of our parser. This repre- 
sents a shift in our thinking about representations 
that has been going on for the last few years. In 
Schank and Abelson (1977), we proposed an addi- 
tional level of representation, called the Knowledge 
Structure level, that represented larger structures of 
information than were available in our original view 
of Conceptual Dependency. In Schank and Carbo- 
nell (1978), we proposed yet another addition to 
our representational system to handle social and 
political acts that were handled rather poorly in the 
previous systems. We have, of course known that 
were a great many issues that could not be ade- 
quately represented in Conceptual Dependency. The 
need for additional representational schemes has 
been, and still is, obvious. But previously, we have 
always attempted to parse into Conceptual Depen- 
dency first, preferring to write our inference mecha- 
nisms so as to begin with input represented in Con- 
ceptual Dependency. This had two main advantages. 
First, it allowed the large number of people, and 
programs that they built, that were working in our 
project to be able to communicate with one another. 
Conceptual Dependency was a kind of interlingua, 
or conceptual Esperanto, in terms of which everyone 
could communicate. Secondly, aside from this prag- 
matic advantage, we believed that this kind of mod- 
ularity was correct from a theoretical point of view. 
Simply stated, we believed that meanings were ex- 
tracted from sentences and then operated upon by 
other processes. 
The obvious proposal when we invented the two 
additional representational systems referred to above 
was to attempt to parse into them directly. Although 
we still believed that people extracted meanings 
from what they heard, there really was no reason to 
believe that these meanings could have one and only 
one form. If 'want' was best represented in a goal 
related fashion and rather complexly represented in 
Conceptual Dependency, what reason was there to 
believe that one had to go through the complex form 
to get to the simple one? Much of this kind of issue 
has formed the basis of various researchers objec- 
tions to our notion of primitives. In particular, Bo- 
brow and Winograd (1977) have made an issue of 
our primitives from time to time. They have pro- 
posed a notion of variable depth of processing as a 
counterproposal to our primitive representations. In 
a sense the system we have described here makes 
use of that suggestion. Bobrow and Winograd are 
correct when they assert that different levels of 
processing make sense at different times. We disa- 
gree with them on the issue of what constitutes the 
appropriate set of levels. We do not believe either 
words themselves or syntactic notions are ever sensi- 
ble stopping points. But the absence of Conceptual 
Dependency in parts of our final representations 
here concedes the larger point. That is, we agree 
that one ought to go as far as one needs to during 
the understanding process. 
What then of Conceptual Dependency and primi- 
tives? In our parser, Conceptual Dependency is used 
as a kind of internal language used in situations 
where the final representation is not apparent. Its 
American Journal of Computational Linguistics, Volume 6, Number 1, January-March 1980 29 
Roger C. Schank, Michael Lebowitz, and Lawrence Birnbaum An Integrated Understander 
use can allow conceptually based inferences to be 
made. Strangely enough, it has begun to bear a cer- 
tain similarity to our use of syntax in the parsing 
process. That is, it is something that is there behind 
the scenes doing its job without ever surfacing 
much. 
The major conclusion of all this then is that we 
believe that modular systems will eventually fall 
apart from their own cumbersomeness. Human-like 
understanding systems must be integrated to the 
extent that they can be guided by their inherent 
interests, delving into what they fancy and skipping 
what they do not. This must be truly what is meant 
by variable depth of processing. Another way of 
saying this is that if we actually pay equal and de- 
tailed attention to everything we are called on to 
understand, we may never finish the understanding 
process. To get all the inferences and relevant 
knowledge structures out all the time may be at the 
worst impossible and at the best unrealistic in terms 
of processing time. A language understander is guid= 
ed by what he wants to know (and what he does not 
want to know). This enables him to not see all the 
ambiguities, triple meanings, myriad implications and 
other problems with what he hears. But what he 
loses in perfection he more than makes up for in 
speed and lack of fragility. Perhaps it is time to 
give our machines the same advantages. 

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