H~ADING WITH A PURPOSE 
Michael Lebowitz 
Department of Computer Science, Yale University 
1. iNTRODUCTION 
A newspaper story about terrorism, war, politics or 
football is not likely to be read in the same way as a 
gothic novel, college catalog or physics textbook. 
Similarly, tne process used to understand a casual 
conversation is unlikely to be the same as the process 
of understanding a biology lecture or TV situation 
comedy. One of the primary differences amongst these 
various types of comprehension is that the reader or 
listener will nave different goals in each case. The 
reasons a person nan for reading, or the goals he has 
when engaging in conversation wlll nave a strong affect 
on what he pays attention to, how deeply the input is 
processed, and what information is incorporated into 
memory. The computer model of understanding described 
nere addresses the problem of using a reader's purpose 
to assist in natural language understanding. This 
program, the Integrated Partial Parser (IPP) ~s designed 
to model the way people read newspaper stories in a 
robust, comprehensive, manner. IPP nan a set of 
interests, much as a human reader does. At the moment 
it concentrates on stories about International violence 
and terrorism. 
IPP contrasts sharply wlth many other tecnniques which 
have been used in parslng. Most models of language 
processing have had no purpose in reading. They pursue 
all inputs with the same dillgence and create the same 
type of representation for all stories. The key 
difference in IPP is that it maps lexlcal input into as 
high a level representation as possible, thereby 
performing the complete understanding process. Other 
approaches have invariably first tried to create a 
preliminary representation, often a strictly syntactic 
parse tree, in preparation for real understandlng. 
~ince high-level, semantic representations are 
ultimately necessary for understanding, there is no 
obvious need for creating a preliminary syntactic 
representation, which can be a very difficult task. The 
isolation of the lexlcal level processing from more 
complete understanding processes makes it very difficult 
for hlgn level predictions to influence low-level 
processing, which is crucial in IPP. 
One very popular technique for creating a low-level 
representation of sentences has been the Augmented 
Transition NetworX (ATN). Parsers of this sort have 
been discussed by Woods \[ 11\] and Kaplan \[SJ. An 
ATN-IiKe parser was developed by Winograd \[10\]. Most 
ATN parsers nave dealt primarily wltn syntax, 
occasionally checking a" few simple semantic properties 
of words. A more recent parser wnicn does an isolated 
syntactic parse was created by Marcus \[4\]. TOe 
important thing to note about all of these parsers is 
that they view syntactic parsing as a process to be done 
prior to real understanding. Even thougn systems of 
this sort at times make use of semantic information, 
they are driven by syntax. Their ~oal of developing a 
syntactic parse tree is not an explicit part of the 
purpcse of human understanding. 
the type of understanding done by IPP is in some sense a 
compromise between the very detailed understanding of 
This work was supported in part by the Advanced Research 
8roJects A~enoy of the Department of Defense and 
monitored under the Office of Naval Research under 
contract N00014-75-C-1111. 
SAM Ill and P~M \[9\], both of which operated in 
conjunction with ELI, Riesbeck's parser \[SJ, and the 
skimming, highly top-down, style of FRUMP \[2\]. EL1 was 
a semantically driven parser which maps English language 
sentences into the Conceptual Dependency \[6\] 
representations of their meanings, it made extensive 
use of the semantic properties of the words being 
processed, but interacted only slightly with the rest of 
the understanding processes it was a part of. it would 
pass off a completed Conceptual Dependency 
representation of each sentence to SAM or PAM which 
would try to incorporate it into an overall story 
representation. BOth these programs attempted to 
understand each sentence fully, SAM in terms of scripts, 
PAM in terms of plans and goals, before going onto the 
next sentence. (In \[~\] Scnank and Abelson describe 
scripts, plans and goals.) SAM and PAM model the way 
people might read a story if they were expecting a 
detalied test on it, or the way a textbook might be 
read. £acn program's purpose was to get out of a story 
every piece of informatlon possible, fney treated each 
piece of every story as being equally important, ~nd 
requiring total understanding. Both of these programs 
are relatively fragile, requiring compiex dictionary 
entries for every word they might en0ounter, as well as 
extensive Knowledge of the appropriate scripts and 
plans. 
FRÙMP, in contrast to SAM and rAM, is a robust system 
whlcn attempts to extract the amount of information from 
a newspaper story which a person gets when ne skims 
rapidly. It does this by selecting a script to 
represent the story and then trying to fill in the 
various slots which are important to understand the 
story. Its purpose is simply to obtain enough 
information from a story to produce a meaningful 
summary. FRUMP is strongly top-down, and worries about 
incoming information from the story only insofar ~s it 
helps fill In the details of the script which it 
selected. 50 wnile FRUMP is robust, simply skipping 
over words it doesn't Know, it does miss interesting 
sections of stories which are not explained by its 
initial selection of a script. 
18P attempts to model the way people normally read a 
newspaper story. Unlike SAM and PAH, it does not care 
if it gets every last plece of information out of a 
story. Dull, mundane information is gladly ignored. 
But, In contrast with FRUMP, it does not want to miss 
interesting parts of stories simply because tney do not 
mesh with initial expectations. It tries to create a 
representation which captures the important aspects of 
each story, but also tries to minimize extensive, 
unnecessary processing which does not contrlbute to the 
understanding of the story. 
Thus IFP's purpose is to decide wnat parts of a story, 
if any, are interesting (in IPP's case, that means 
related to terrorism), and incorporate the appropriate 
information into its memory. The concepts used to 
determine what is interesting are an extension of ideas 
presented by SctmnK \[7\]. 
2. How l~ EOA~s 
The ultimate purpose of reading a newspaper story is to 
incorporate new information into memory. In order to do 
this, a number of different Kinds of Knowledge are 
needed. The understander must Know the meanings of 
words, llngulatic rules about now words combine into 
sentences, the conventions used in writing newspaper 
5g 
stories, and, crucially, have extensive knowledge about 
the "real world." It is impossible to properly 
understand a story without applying already existing 
knowledge about the functioning of the world. This 
means the use of long-term memory cannot be fruitfully 
separated from other aspects of the natural 
understandin~ problem. The mana~emant of all this 
information by an understander is a critical problem In 
comprehension, since the application of all potentially 
relevant Knowledge all the time, would seriously degrade 
the understandin~ process, possibly to the point of 
halting It altogether. In our model of understanding, 
the role played by the interests of the understander Is 
to allow detailed processing to occur only on the parts 
of the story which are Important to overall 
understanding, thereby conserving processing resources. 
Central to any understandin~ system is the type of 
Knowledge structure used to represent stories. At the 
present time, IPP represents stories in terms of scripts 
similar to, although simpler than, those used by SAM and 
FRUMP. Most of the co--on events In IPP's area of 
Interest, terrorism, such as hiJaokings, kidnappings, 
and ambushes, are reasonanly stereotyped, although not 
necessarily wltn all the temporal sequencing present in 
the scripts SAM uses. ZPP also represents some events 
directly In Conceptual Dependency. The representations 
in IPP consist of two types of structures. There are 
the event structures themselves, generally scripts such 
as $KIDNAP and SAMBUSH, which form the backbone of the 
story representations, and tokens which fill the roles 
in the event structures. These tokens are basically the 
?tcture Producers of \[6\], and represent the concepts 
underlying words such as "airliner," "machine-gun" and 
"Kidnapper." The final story representation can also 
Include links between event structures indicating 
causal, temporal and script-scene relationships. 
Due to IPP's limited repertoire of structures with which 
to represent events, it is currently unable to fully 
understand some stories which maXe sense only in terms 
of goals and plans, or other higher level 
representations. However, the understanding techniques 
used in IPP should be applicable to stories which 
require the use of such knowledge structures. This is a 
topic of current research. 
It Is worth noting that the form of a story's 
representation may depend on the purpose behind its 
being read. If the reader is only mildly Interested in 
the subject of the story, soriptal representation may 
well be adequate. On the other hand, for an story of 
great interest to the reader, additional effort may be 
expended to allow the goals and plans of the actors In 
the story to be gorked out. This Is generally more 
complex than simply representing a story in terms of 
stereotypical knowledge, and will only be attempted in 
cases of great interest. 
In order to achieve its purpose, ~PP does extensive 
"top-down" processing. That Is, It makes predlotions 
aOout what it is likely to see. These predictions range 
from low-level, syntactic predictions ("the next noun 
phrase will be the person kidnapped," for instance) to 
quite high-level, global predictions, ("expect to see 
demands made by the terrorist"). Significantly, the 
program only makes predictions about things it would 
like to Know. It doesn't mind skipping over unimportant 
parts of the text. 
The top-down predictions made by IPP are implemented in 
terms of requests, similar to those used by RiesbecK 
\[5\], which are basically Just test-action pairs. While 
such an implementation In theory allows arbitrary 
computations to ~e performed, the actions used in IPP 
are in fact quite limited. IPP requests can build an 
event structure, link event structures together, use a 
token to fill a role in an event structure, activate new 
requests or de-activate other active requests. 
The tests in IPP requests are also llmited in nature. 
They can look for certain types of events or tokens, 
check for words with a specified property in their 
dictionary entry, or even check for specific lexical 
items. The tests for lexical items are quite Important 
in Keeping IPP's processing efficient. One advantage is 
that very specific top-down predictions will often allow 
an otherwise very complex word disa~biguation process to 
be bypassed. For example, in a story about a hijacking, 
ZPP expects the word "carrying" to indicate that the 
passengers of the hijacked vehicle are to follow. So it 
never has to consider An any detail the meaning of 
"carrying." Many function words really nave no meaning 
by themselves, and the type of predictive processing 
used by IPP is crucial in handling them efficiently. 
Despite its top-down orientation, IPP does not ignore 
unexpected Input. Rather, If the new Information is 
interesting in itself the program will concentrate on 
it, makin~ new predictions In addition to, or instead 
of, the original ones. The proper integration of 
top-down and bottom-up processing allows the program to 
be efficient, and yet not miss interesting, unexpected 
information. 
The bottom-up processin~ of IPP is based around a 
ulassification of words that is done strictly on the 
basis of processing considerations. IPP Is interested 
in the traditional syntactic classifications only when 
they help determine how worqs should be processed. 
IPP's criteria for classification Involve the type of 
data structures words build, and when they should be 
processed. 
Words can build either of the main data structures used 
in XPP, events and tokens. The words bulldin~ events 
are usually verbs, but many syntactic nouns, such as 
• kidnapping," "riot," and "demonstration" also indicate 
events, and are handled in Just the same way as 
traditional verbs. Some words, such as =oat adjectives 
and adverbs, do not build structures but rather modify 
structures built by other words. These words are 
handled according to the type of structure they modify. 
The second criteria for classifying words - when they 
should be processed - is crucial to 1PP's operation. In 
order to model a rapid, normally paced reader, IPP 
attempts to avoid doin~ any processing which will not 
add to its overall understandin~ of a story. To do 
this, it classifies words into three groups - words 
which must be fully processed i--edlately, words which 
should be saved in short-ter~ memory, and then processed 
later, if ne,=essary, and words which should be skipped 
entirely. 
Words which must be processed immediately include 
interesting words building either event structures or 
tokens. "Gunmen," "kidnapped" and "exploded" are 
typical examples. These words give us the overall 
framework of a story, indicate how much effort should 0e 
devoted to further analysis, and, most importantly, 
generate the predictions w~loh allow later processing to 
proceed efficiently. 
The save and process later words are those which may 
become si~nifioant later, but are not obviously 
impor~cant when they are read. This class is quite 
substantial, Including many dull nouns and nearly all 
adjectives and adverbs. Zn a noun phrase sucn as 
"numerous Italian gunmen," there Is no point in 
processing tO any depth "numerous" or "Italian" until we 
~now the word they modify is Important enou~n to be 
included in the final representation. Zn the cases 
where further procesein~ is necessary, IPP has the 
proper information to easily incorporate the saved words 
Into the story representation, and In the many cases 
60 
where the word is not important, no effort above saving 
the word is required. The processin~ strategy for these 
words is a Key to modei~n~ nom,al reading. 
The final class of words are those IPP skips altogether. 
Thls class includes very unlnterestln~ words whlch 
neither contribute processing clues, nor add to the 
story representation. Many function words, adjectives 
and verbs irrelevant to the domain at hand, and most 
pronouns fall into this category. These words can still 
be significant in cases where they are predlcted, but 
otherwise they are ignored by IPP and take no processln~ 
effort. 
In addition to the processing techniques mentioned so 
far, IPP makes use of several very pragmatic heuristics. 
These are particularly important in processlng noun 
~roups properly. An example of the type of heuristic 
used is IPP's assumption that the first actor in a story 
tends to be important, and is worth extra processing 
effort. Other heurlst~cs can be seen in the example In 
section ~. IP~'s basic strategy is to make reasonable 
guesses about the appropriate representation as qulcKly 
as possible, facilitating later processln~ and fix 
things later if its ~uesses are prove to be wrong. 
~. ~ DETAILED ~XAMPLE 
~n order to illustrate bow IPP operates, and how its 
purpose affects its process|n{, an annotated run of IPP 
on a typical story, one taken from the Boston Globe is 
shown below. The text between the rows of stars has 
been added to explain the operation of IPP. Items 
beginning with a dollar sign, such as $rERRORISM, 
indicate scripts used by IPP to represent events. 
\[PHOTO: Initiated Sun 24-Jun-79 3:36PM\] 
@RUN IPP 
*(PARSE $1) 
Input: $1 (3 I~ 79) IRELAND 
(GUNMEN FIRING FROM AMBUSH SERIOUSLY WOUNDED AN 
8-YEAR-OLD GIRL AS SHE WAS BEING TAKEN TO SCHOOL 
YESTERDAY AT STEWARrSTOWN COUNTY r~RONNE) 
Processing: 
GUNMEN : InterestinE token - GUNMEN 
Predictions - SHOOTING-WILL-OCCUR ROBBERY-SCRIPT 
TERRORISM-SCRIPT HIJACKING-SCRIPT 
lll**lem*llllll*l*mli,lll,l,lll,l,mllll,mlm,lllilmm,illl 
GUNMEN is marked In the dlotionary as inherently 
interesting. In humans this presumably occurs after a 
reader has noted that stories involving gunmen tend to 
be interesting. Since it is interesting, IPP fully 
processes GUNMEN, Knowing that it Is important to its 
purpose of extracting the significant content of the 
story, it builds a token to represent the GUNMEN and 
makes several predlctlons to facilitate later 
processing. There is a strong possibility that some 
verb conceptually equivalent to "shoot" will appear. 
There are also a set of scripts, including SROBBERY, 
STERRORISM and $HIJACK wnlcn are likely to appear, so 
IPP creates predictions looking for clues indicating 
that one of these scripts sOould be activated and used 
to represent the story. 
FIRING : Word satisfies prediction 
Prediction confirmed - SHOOTING-WILL-OCCUR 
Instantiated $SHOOT script 
61 
Predictions ° $SHOOf-HUL::-FINUER REASON-FOR-SHOOtING 
$SHoor-scEN~S 
tJeiIJ~i~Jf~mmQll~l|l#~Oilm~i~Ome|J|i~|~i~iQltllliJIDI 
FIHING satisfies the predlction for a "shoot" verb. 
Notice that tne prediction immediately dlsamblguates 
FIRING. Other senses of the word, such as "terminate 
employment" are never considered. Once IPP has 
confirmed an event, it builds a structure to represent 
it, in this case the $SHOOr script and the token for 
GUNMEN is filled in ss the actor. Predictions are made 
trying to flnd the unknown roles of the script, VICTIM, 
in particular, the reason for the shooting, and any 
scenes of $SHOOT wnicn might be found. 
JJJiJJJJJiJiJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJlJJJJJJJJJJJJJ 
instantiated $ATTACK-P~RSON script 
Predictions - SAT rACK-PERSON-ROLE-FINDER. 
SATrACK-PERSON-SC~N~S 
Im,*|i@m|li,I@Wm~#mI~@Igm#wIiII#mmimmIII|@milIIillJgimR@ 
IPP does not consider the $SHOOT script to be a total 
explanation of a snootin~ event. It requires a 
representation wnlcn indicates the purpose of the 
various actors, in the absence of any other 
information, IPP assu~es people wno shoot are 
deliberately attacKin~ someone. So the SATTACK-PERSON 
script is Inferred, and $SHOOT attacned to it as a 
scene. The SATTACK-PERSON representation allows IPP to 
make inferences which are relevant to any case of a 
person being attacked, not just snootin~s. IPP is still 
not able to Instantiate any of the high level scripts 
predicted by GUNMEN, since the SATTACK-PERSON script is 
associated with several of the~. 
FROM : Function word 
Predictions - FILL-FROM-SLOT 
Ji*JiJJeJ**JJJJiJJJJJJJlJJJJJJJJJ*JJJJ*JJJJ**J*JJJJJ*J*J 
FROM in s =ontext such as this normally indicates the 
location from which the attack was made is to follow, so 
IPP makes a prediction to that effect. However, since a 
word building a token does not follow, the prediction is 
deactivated. The fact that AMBUSH is syntactically a 
noun is not relevant, since iFP's prediction loo~s for a 
word which identifies a place. 
li*JiJJ*Jll**J*lJli|iJl*lii|llll#*J**JiJJiJJ**iJil*iiJJ* 
AMBUSH : Scene word 
Predictions - SAMBUSH-ROL~-FIND~R $AMBUSH-SCENKS 
Prediction confirmed - TERRORISM-SCRIPT 
Instantlated $TERRORISM script 
Predictions - TERRORIST-DEMANDS STERRORISM-ROLE-FINDER 
STERRORISM-SCENES COUNTER-MEASURES 
J*lJJJ*JiJJJJJJiJ*JJJJJJlJJJJJJJJJ*JJJi*JJ*JJJJ***JJJJ** 
IPP <nows the word AMBUSH to indicate an instance of the 
SAMBUSH scr|pt, and tn~t SAMBUSH can be a scene of 
$TERRORISM (i.e. it is an activity w~Ich can be 
construed as a terrorist act). This causes the 
prediction made by GUNMEN that $TERRORISM was a possible 
script tO be trlggerred. Even if AMBUSH had other 
meanings, or could be associated with other higher level 
scripts, the prediction would enable quicK, accurate 
identification and incorporation of the word's meaning 
into the story representation. IPP's purpose of 
associating the shooting with a nlgh level Knowledge 
structure which helps to expialn it, has been achieved. 
At this point in the processing an Instance of 
STERRORISM is constructed to serve as the top level 
representation of the story. The SAMBUSH and 
SATTACK-PERSON scripts are attached as scenes of 
STERRORISM. 
SgRIOUSLY : SKip and save 
~OUNO£D : Word satisfies prediction 
Prediction confirmed - SWOUND-SCENE 
Predictions - SWOUND-ROLE-FINDER SWOUND-SCENES 
t~e~eoeeeleleeeeeeelloeelem|eee|eoeeeeaoalenlo|eleeoeeee 
SWOUND is a Known scene of $ATTACK-PERSON, representin~ 
a common outcome of an attack. It is instantlated and 
attached to $ATTACK-P~RSON. IPP infers that the actor 
of SWOUND is probably the same as for $A~ACK-PERSON, 
i.e. the GUNMgN. eleileleleeeelllllll|lllalllolsllieilllOlllelllel|oileil 
AN : SKip and save 
~-YEAR-OLD : Skip and save 
GiRL : Normal token - GIRL 
Prediction confirmed - SWOUND-ROLE-FINDER-VICTIM 
eeee~eeeeeeme~eee~see~e~eee~m~ee~o~eeeeeeeeeee~aeeoee 
~IRL Ouilds a toXen wnlch fllls t~e VICTIM role of the 
SWOUND script. Since IPP has inferred that the VICTIM 
of the ~ATrACK-PERSON and SSHOOr scripts are the same as 
the VICTIM of SWOUND, it also fills in those roles. 
Identifyin~ these roles is integral to IFP's purpose of 
understanding the story, since an attack on a person can 
only Oe properly understood if the victim is Known. As 
t~is person is important to the understandln~ of the 
story, IPP wants to acquire as much information as 
possible about net. Therefore, it looks baoK at the 
modifiers temporarily saved in short-term memory, 
8-YEAR-OLD in this case, and uses them to modify the 
token ~uilt for GIRL. The age of the ~Irl is noted as 
eight years. This information could easily be crucial 
to appreciatin~ the interesting nature of the story. 
@EeE~eeBe@~oeeEeeeeeeeE~e~aEeeoaeEsasee|eaeeeeeeeeEssee 
AS : SKip 
SHE : SKip 
WAS : SKip and save 
BEING : Dull verb - skipped 
TAKEN : SKip 
TO : Function word 
SCHOOL : Normal token - SCHOOL 
Y~ST~RDAY : Normal token - YESTERDAY 
~eee~ene~e~e~neeeeeaeeeeoeeeeeeeaeeeeeaeeeeeeeeeeeeeeee 
Nothin~ in this phrase is either inherently interesting 
or fulfills expectations made earlier in the processing 
of the story. So it is all prc,:essed very 
superficially, addin~ nothing to the final 
representation. It is important that IPP ma~es no 
attempt to dlsamOi~uate words such as TAKEN, an 
extremely complex process, since it knows none of the 
possible meanings will add significantly to its 
understanding. 
@illIIIIIIIIIIIIIIIIIIIIIIIllOIIlllIIIIIiilIIIIIIIIilIII 
AT : Function word 
STEWARTSTOWN : Skip and save 
COUNTY : SKip and save 
TYRONNE : Normal token - TYRONNE 
Prediction confirmed - $T~RRORISH-ROLE-FIHDER-PLACE 
emmtu~u~eeeeteHeJ~eee~t~e~eeeeatteet~aaeaaeaeeesewaa 
ST£WARTSTOWN COUNTY rYRONNE satisfies the ?redlotlon for 
the place where the terrorism took plane. IPP has 
inferred that all the scenes of the event took place at 
the same location. IPP expends effort in identifying 
this role, as location is crucial to the understandln~ 
of most storles. It is also important in the 
or~anizatlon of memories about stories. A incidence of 
terrorism in Northern ireland is understood differently 
from one in New York or Geneva. 
62 
Story Representation: 
ee MAIN \[VENT ee 
SCRIPT $TERRORISM 
ACTOR GUNMEN 
PLACE $TEWARTSTOWN COUNTY TYRONNE 
TIHE ~ESTERDAY 
SCENES 
SCRIPT SAHBUSH 
ACTOR GUNMEN 
SCRIPT $ATTACK-PERSON 
ACTOR GUNMEN 
VICTIM 8 ~EAR OLD GIRL 
SCENES 
SCRIPT $SHOOT 
ACTOR GUNMEN 
VICTIM 8 XEAR OLD GIRL 
SCRIPT SWOUND 
ACTOR GUNMEN 
VICTIM 8 YEAR OLD GIRL 
EXTENT GREATERTHAN-nNORH e 
saesaeeeaeeeeseeeeeeeeeesseeesesesaeaeeoeeeeaeeeeeaeeeee 
IPP's final representation indicates that it has 
fulfilled its purpose in readimi the story. It has 
extracted roughly the same information as a person 
reading the story quickly. IPP has r~ognised an 
instance of terrorism oonststln8 of an ambush in whioh 
an eight year-old girl was wounded. That seems to be 
about all a person would normally remember from suoha 
story. eseeeeeeeeeae|eeeeeeesneeeeeaeeeeeeeeeeseeeeeeeaeeeeeese 
\[PHOTO: Terminated Sun 24-jun-79 3:38~\] 
As it pro~esses a story such as this one, IPF keeps 
track of how interesting it feels the story is. Novelty 
and relevance tend to increase interestlngness, while 
redundancy and irrelevance dec?ease it. For example, in 
the story shown moore, the faot that the victim of the 
shooting was an 8 year-old ingresses the interest of the 
story, and the the incident taMin~ place in Northern 
Ireland as opposed to a more unusual sate for terrorism 
decreases the interest. The story's interest Is used to 
determine how much effort should be expended in tryin~ 
to fill in more details of t~e story. If the level of 
lnterestingness decreases fax' enough, the program can 
stop processing the story, and look for a more 
interesting one, in the same way a person does when 
reading through a newspaper. 
~. ANOTHER EXAMPLE 
The following example further illustrates the 
capabilities of IPP. In this example only IPP's final 
story representation is snows. This story was also 
taken from the Boston Globe. 
\[PHOTO: Initiated Wed 27-Jun-79 I:OOPM\] 
@RUN IPP 
°(PARSE S2) 
Input: S2 (6 3 79) GUATEMA~t 
(THE SON OF FORMER PRESIDENT EUGENIC KJELL LAUGERUD 
WAS SHOT DEAD B~ UNIDENTIFIED ASSAILANTS LAST WEEK 
AND A BOMB EXPLODED AT THE HOME OF A GOVERNMENT 
OFFICIAL ~LICE SAID) 
Story Representation: 
am MAIN EVENF ea 
SCRIPT STERRORISM 
ACTOR UNKNOWN ASSAILANTS 
SCENES 
SCRIPT $ATTACK-PERSON 
ACTOR UNKNOWN ASSAILANTS 
VICTIM SON OF PREVIOUS PRESIDENT 
EUGENIC KJELL LAUG~RUD 
SCENES 
SCRIPT $SHOOT 
ACTOR UNKNOWN ASSAILANTS 
VICTIM SON OF PREVIOUS PRESIDENT 
EUGENIC KJELL LAUGERUD 
SCRIPT SKill 
ACTOR UNKNOWN ASSAILANTS 
VICTIM SON OF PREVIOUS PRESIDENT 
EUGENIC KJELh LAUG~RUD 
SCRIPT SATTACK-PLAC£ 
ACTOR UNKNOWN ASSAILANTS 
PLACE HOME OF GOVERNMENT OFFICIAL 
SC~NdS 
SCRIPT $BOHB 
ACTOR UNKNONN ASSAILANTS 
PLACE HOME OF GOVERNMENT OFFICIAL 
\[PHOTO: Terminated - Wed 27-Jun-79 I:09PM\] 
Thls example maces several interesting points about the 
way IPP operates. Notice that 1PP has jumped to a 
conclusion about the story,, which, while plausible, 
could easily be wrong, it assumes that the actor of the 
SBOMB and SATTACK-PLACE scripts is the same as the actor 
of the STERRORISM script, which was in turn inferred 
from the actor of the sbootln~ incident. Tnls is 
plausible, as normally news stories are about a coherent 
set of events witn lo~Ical relations amongst them. So 
it is reasonable for a story to De about a series of 
related acts of terrorism, committed by the same person 
or ~roup, and tnat is what IPP assumes here even though 
that may not be correct. Uut this ~Ind of inference is 
exactly the Kind which IPP must make in order to do 
efficient top-down processln~, despite the possibility 
of errors. 
The otner interesting point about tnis example is the 
way some of iPP's quite pragmatic heuristics for 
processln~ give positive results. For instance, as 
mentioned earlier, the first actor mentioned has a 
stronz tendency to be important to the understandln~ of 
a story. In thls story that means that the modlfyin~ 
prepositional phrase "of former President Su~enlo Kjell 
Lau~erud" is analyzed and attached to the token built 
for "son," usually not an interesting word. Heur~stlcs 
of this sort ~ive IPP its power and robustness, rather 
than any single rule about language understandln~. 
5. CONCLUSION 
IPP has been implemented on a DECsystem 20/50 at Yale. 
It currently has a vocabulary of more than I~00 words 
wnlcn is oelng continually Increased in an attempt to 
make the program an expert underst~der of newspaper 
stories scout terrorism. £t is also planned to add 
information about nigher level knowledge structures such 
as ~oals and plans and expand IPP's domain of interest. 
To date, IPP has successfully processed over 50 stories 
taken directly from various newspapers, many sight 
unseen. 
The difference between the powers of IPP and the 
syntactlcally driven parsers mentioned earller can cent 
be seen by the Kinds of sentences they handle. 
Syntax-0ased parsers generally deal with relatively 
simple, syntactically well-formed sentences. IPP 
handles sucn sentences, Out also accurately processes 
stories taken directly from newspapers, which often 
involve extremely convoluted syntax, and in many cases 
are not grammatical at all. Sentences of this type are 
difficult, if not impossible for parsers relyln~ on 
syntax. IPP is sole to process news stories quickly, on 
the order of 2 CPU seconds, and when done, it has 
achieved a complete understandln~ of the story, not Just 
a syntactic parse. 
As shown in tne examples above, interest can provide a 
purpose for reading newspaper stories. In other 
situations, other factors might provide the purpose. 
But the purpose is never simply to create a 
representation - especially a representation with no 
semantic content, such as a syntax tree. This is not to 
say syntax is not important, obviously in many 
circumstances it provides crucial information, but it 
should not drive the understanding process. Preliminary 
representations are needed only if they assist in the 
reader's ultimate purpose bulldln~ an appropriate, 
high-level representation which can be incorporated with 
already existing Knowledge. The results achieved by IPP 
indicate that parsing directly into high-level knowledge 
structures is possible, and in many situations may well 
be more practical than first doin~ a low-level parse. 
Its integrated approacn allows IPP to make use of all 
the various kinds of knowledge which people use when 
understandtn~ a story. 
References 
\[1\] Cullin&ford, R. (1978) Script application: 
Computer understanding of newspaper stories. 
Research Report 116, Department of Computer 
Science, Yale University. 
\[2\] DeJon~, G.F. (19/9) Skimming stories in real 
time: An experiment in integrated understanding. 
Research Report 158, Department of Computer 
Science, Yale University. 
\[3\] Kaplan, R.M. (1975) On process models for 
sentence analysis, in D.A. Norman and 
D. E. R~elhart, ads., Explorations in ~oanition. 
W. H. Freeman and Company, San Francisco. 
\[~\] Marcus, M.P. (1979) A Theory of Syntactic 
Recognition for Natural Language, in P H . 
Winston and R.H. Brown (eds.), Artificial 
IntellJ~ence: an ,~ Presnectlve, HIT Press, 
Cambridge, Massachusetts. 
\[5\] Riesbeck, C. K. (1975) Conceptual analysis. In 
R.C. ScnanK (ed.),. ~ Information 
Processing. North Holland, Amsterdam. 
\[6\] Scnank, R.C. (1975) Conceotual Information 
Processln¢. North Holland, Amsterdam. 
\[7\] Scnank, R. C. (1978) Interestlngness: Controlling 
inferences. Research Report I~5, Department of 
Computer Science, Yale University. 
\[8\] Scbank, R. C. and Abelson, R. P. (1977) Scrints. 
Plans, Goals and Understanding. Lawrence grlbaum 
Associates, Rlllsdale, New Jersey. 
\[9\] dllensky, R. (1978) Understanding goal-based 
stories. Research Report I~0, Department of 
Computer Science, Yale University. 
\[10\] Wtnograd, T. (1972) Understandin~ Natural 
Lan:uafe. Academic Press, New York. 
\[11\] ~oods, W. A. (1970) Transition network grammars 
for natural language analysis. ~of 
the ACH. Vol. 13, p 591. 
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