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AN EXAMPLE FOR NATURAL
LANGUAGE UNDERSTANDING AND THE
AI PROBLEMS IT RAISES
Computer Science Department
Stanford, CA 94305
1 THE STORY
The following story from the New York Times is my candidate for a target
for a natural language understander. The story is about a real world event,
and therefore the intentions of the author are less relevant for answering
questions than for made up stories. The main goal of this discussion is to say
what a person who has understood the story knows about the event. This
seems to me to be preliminary to making programs that can understand.
“A 61-year old furniture salesman was pushed down the shaft of
a freight elevator yesterday in his downtown Brooklyn store by two
robbers while a third attempted to crush him with the elevator car
because they were dissatisﬁed with the $1,200 they had forced him to
The buﬀer springs at the bottom of the shaft prevented the car
from crushing the salesman, John J. Hug, after he was pushed from
the ﬁrst ﬂoor to the basement. The car stopped about 12 inches above
him as he ﬂattened himself at the bottom of the pit.
Mr. Hug was pinned in the shaft for about half an hour until his
cries attracted the attention of a porter. The store at 340 Livingston
Street is part of the Seaman’s Quality Furniture chain.
Mr. Hug was removed by members of the Police Emergency Squad
and taken to Long Island College Hospital. He was badly shaken, but
after being treated for scrapes of his left arm and for a spinal injury
was released and went home. He lives at 62-01 69th Lane, Maspeth,
He has worked for seven years at the store, on the corner of Nevins Street,
and this was the fourth time he had been held up in the store. The last time
was about one year ago, when his right arm was slashed by a knife-wielding
An intelligent person or program should be able to answer the following
questions based on the information in the story:
would have probably been stated. What did the porter say to the robbers?
Nothing, because the robbers left before he came.
in Brooklyn? No, he lives in Queens.
his cries were heard.
springs? Mr. Hug would have been crushed.
10. Did Mr. Hug want to be crushed? No.
11. Did the robbers tell Mr. Hug their names? No.
12. Were the robbers present when the porter came? No.
13. Did Mr. Hug like the robbers, and did they like him?
14. Why did the robbers leave without killing Mr. Hug? Perhaps, they
thought they had killed him, and perhaps their anger was appeased by the
actions they had performed, and perhaps they had taken all the time they
dared, and perhaps something speciﬁc happened to frighten them away.
15. What would have happened if Mr. Hug had tried to run away?
Perhaps he would have succeeded, but more likely they would have injured
or killed him since probably they had weapons, and there were three of them.
16. What can Mr. Hug do to avoid this in the future? No solution is
entirely satisfactory. He could carry a gun or he could quit or he could get
his employers to install an alarm system or maybe he will be lucky.
17. Did Mr. Hug know he was going to be robbed? Does he know that
he was robbed?
18. Was Mr. Hug’s right arm slashed before his left arm was scratched?
Yes, because the former was a year ago.
19. How did the robber try to crush him with the car? By pressing the
buttons or operating the control lever to make the car go to the bottom of
20. Why did Mr. Hug yell from the bottom of the elevator shaft? So as
to attract the attention of someone who would rescue him.
21. How long did the events take? More than half an hour but less than a
day. Most of the time was spent by Mr. Hug ﬁlling out forms in the hospital.
22. What crimes were committed? This question has the advantage that
it is one that is normally answered on the basis of such a story, since the police
report of the incident was probably the basis of the New York Times story.
Robbery, possibly assault with a deadly weapon, and attempted murder are
the more obvious crimes. One might speciﬁcally challenge natural language
systems to answer this question.
The above list of questions is rather random. I doubt that it covers all
facets of understanding the story. It would be worthwhile to try to make up
a list of questions that does cover substantially all aspects of the story in
order to get as complete as possible an intuitive idea of what capabilities are
involved in understanding such a story.
Note that the story is about a real event so that such a question as what
does the “J” in “John J. Hug” stand for has an answer.
In a made-up
story, questions about middle names or what year the story occurred in do
not necessarily have an answer, and an intelligent person or program would
know that too.
2 ARTIFICIAL NATURAL LANGUAGE
I think that artiﬁcial intelligence is not very close to being able to understand
such stories in a genuine way. Therefore, I would like to sneak up on it grad-
ually by dividing the problem into parts which can be attacked separately.
Here are some of the components:
such a formalism would be to produce a program for translating from the
formalism into any of several natural languages. More weakly, it should be
as easy for a human to translate from the formalism into a natural language
as to translate from one known natural language to another. Let’s call this
formalism an artiﬁcial natural language—ANL for short.
The grammar of ANL should be trivial and mathematical in character.
There would be an “English” version in which English words were used as
identiﬁers, but there would still have to be a glossary that gives the precise
meaning of the identiﬁers. There would also be a German and a Japanese
version. The translation from the English version to the German or Japanese
version would be a simple substitution for identiﬁers, and a German or
Japanese who had learned the grammar could then translate into his lan-
guage with the aid of the German or Japanese glossary.
This idea has some resemblance to the idea of “deep structure,” but I
have some doubts about whether either idea is well enough deﬁned to say.
Mr. Hug ﬁrst, and what the robbers said even though it is not stated in the
story. Clearly some compromise is necessary here, since the data structure
need not be able to express positions and velocities of molecules. Like the
PLANNER languages, as Robert Moore has characterized them in his 1976
MIT Master’s thesis, the descriptions would contain no disjunctions, and
might be a collection of relations with constants as arguments where every
relation not asserted (in a certain class) is automatically denied.
Alternatively, the basis of this data structure might be various networks of
nodes described by sentences in the predicate calculus. Some of the sentences
would assert that certain programs applied to the data structures would
answer certain questions. When such sentences existed, reasoning would
include the operation of the programs.
In this way, we would expect to
avoid the extreme prolixity that arises when we attempt to do even simple
calculations by pure predicate calculus deduction.
The test of success for the “data structure” would be that a human could
readily formally deduce the answers to the above questions using a proof
checker. Most of the proof-checker would be straightforward, but there is a
major problem concerned with when it is possible to “jump to a conclusion.”
b. An “understander” that constructs the “facts” from a text in the ANL.
c. Expression of the “general information” about the world that could
allow getting the answers to the questions by formal reasoning from the
“facts” and the “general information.” The “general information” would
also contain non-sentence data structures and procedures, but the sentences
would tell what goals can be achieved by running the procedures. In this
way, we would get the best of the sentential and procedural representations
d. A “problem solver” that could answer the above questions on the basis
of the “facts.” We imagine the questions to be expressed in the “fact” lan-
guage and expect the answers in the “fact” language, i.e. we avoid grammar
problems in both understanding the questions and in expressing the answers.
3 THE USE OF LOGICAL FORMULAS
When my understander has digested the story of Mr. Hug, it will have added
one or more predicate calculus sentences to its data base. One sentence will
do if it has the form
∃ep1p2g1g2e1e2 . . . (
name(p1) =(cid:48)(cid:48) J ohnJ.Hug (cid:48)(cid:48) ∧ g1 ⊂ Robbers ∧ . . .).
In this form, all the entities involved in expressing the facts of the story
are existentially quantiﬁed variables. The only constants in the formula
would have been present in the system previously. However, it is probably
better to use a collection of sentences introducing a collection of individual
In this case, there will be 20 or so new individual constants
representing people, groups of people, the main event and its sub-events,
places, organizations, etc.
In representing the robbers, the system has a choice of representing them
by three individual constants, R1, R2, and R3 or by using a single symbol G1
to represent the group of robbers. A good system will probably use both. If
the number of robbers were not speciﬁed, we would have to use a constant for
the group. We have to identify the robber who operated the elevator while
the others pushed Mr. Hug into the shaft. We shall call him R1. The other
two are not discriminated in the story, but there is no harm in our calling
them R2 and R3, even if there is no information to discriminate them. If
there were 20 robbers, it would be a mistake to give them all individual
names. Suppose it had further been stated that as the robbers left one of
them threatened to return and kill Mr. Hug later but that it was not stated
whether this robber was the same one who operated the elevator. We could
designate this robber by R4, but we would not have sentences asserting that
R4 was distinct from R1, R2 and R3; instead we would have a sentence
asserting that R4 was one of these.
It is tempting to identify the group
of robbers with the set R1, R2, R3, but we may want to give the group
some properties not enjoyed by the set of its members. Sentences with plural
subjects express some rather tricky concepts. Thus, the group robbed the
store, and this is not an assertion that each member robbed the store.
The “members of the police emergency squad” presents a similar prob-
lem. We don’t want to assert how many there were. In this connection, it
may be worthwhile to distinguish between what happened and what we wish
to assert about what happened. A language adequate to describe what hap-
pened would not have to leave the number of policemen present vague and
could give them each a name. In my old jargon, such a language would be
metaphysically adequate though not epistemologically adequate. Devising a
language that is only metaphysically adequate may be a worthwhile stage on
the way to an epistemologically adequate system. By “devising a language”
I mean deﬁning a collection of predicate and constant symbols and axioma-
tizing their general properties. This language should not be peculiar to the
story of Mr. Hug, but we should not require that it be completely general in
the present state of the science.
It is not obvious how to express what we know when we are told that
Mr. Hug is a furniture salesman. A direct approach is to deﬁne an abstract
entity called F urniture and a function called salesmen and to assert Hug ∈
This will probably work although the logical connection between the ab-
stract entity F urniture and concrete chairs and tables needs to be worked
out. It would be over-simpliﬁed to identify F urniture with the set of fur-
niture in existence at that time, because one could be a salesman of space
shuttles even though there don’t exist any yet. In my opinion, one should re-
sist a tendency to apply Occam’s razor prematurely. Perhaps we can identify
the abstract F urniture with the an extension of the predicate that tells us
whether an object should be regarded as a piece of furniture, perhaps not. It
does no harm to keep them separate for the time being. This case looks like
an argument for using second order logic so that the argument of salesmen
could be the predicate f urniture that tells whether an object is a piece of
furniture. However, there are various techniques for getting the same result
without the use of second order logic.
4 THE NEED FOR NONMONOTONIC REA-SONING
After reading the story, one is prepared to answer negatively the question of
whether there was someone else besides Mr. Hug and the robbers present.
However, sentences describing such another person could be added to the
story without contradiction. Our basis for the negative answer is that we
can construct a model of the facts stated in the story without such a person,
and we are applying Occam’s razor in order to not multiply entities beyond
necessity. This could be attributed to the fact that the New York Times tells
the whole story when it can, but I think that by putting Occam’s razor into
the system, we can get this result without having to formalize the New York
This suggests introducing the notion of the minimal completion of a story
expressed in the predicate calculus. The minimal completion of the story is
also a set of sentences in the predicate calculus, but it contains sentences
asserting things like “The set of people in the store while the robbers were
trying to crush Mr. Hug consists of Mr. Hug and the robbers.” These sen-
tences are to be obtained from the original set by the application of a process
formalizing Occam’s razor. This process works from a set of sentences and
is not logical deduction although it might be accomplished by deduction in
a meta-language that contained sentences about sets of sentences. As I have
pointed out elsewhere, the process cannot be deduction, because it generates
sentences that contradict sentences that are consistent with the original set
A number of the questions given in the previous section have answers
that can be formally deduced from the minimal completion but not from the
It has been suggested that probabilistic reasoning should be used to ex-
clude the presence of other people rather than Occam’s razor. The problem
with this is that the number of additional entities that are not logically ex-
cluded is limited only by one’s imagination so that it is not clear how one
could construct a probabilistic model that took these possibilities into ac-
count only to exclude them as improbable. If one wants to introduce proba-
bilities, it might make more sense to assign a probability to the correctness of
the minimal completion of a New York Times story based on its past record
in ﬁnding the relevant facts of robberies.
Another problem in constructing the completion is the isolation of the
story from the rest of the world. The members of the Police Emergency
Squad all have mothers (living or dead), but we don’t want to bring them
into the completion—not to speak of bringing in more remote ancestors all
of whom can be asserted to exist on the basis of axioms about people.
To recapitulate: The original set of predicate calculus sentences can be gen-
erated from the story as one goes along. Each sentence is generated approx-
imately from a sentence of the story with the aid of general knowledge and
what has been generated from the previous sentences. (This will usually be
the case if the story is well told although there are sometimes cases in which
the correct way to express a sentence will depend on what follows - but this
is not good writing.) The completion, however, will depend on the whole of
It might be interesting to consider what can be determined from a partial
reading of the story—even stopping the reading in the middle of a sentence
since what has appeared so far in a sentence often must be understood in
order to even parse the re. . .
/@steam.stanford.edu:/u/ftp/jmc/mrhug.tex: begun 1996 May 14, latexed 1996 May 14 at 9:31 a.m.