Some Methodological Issues in 
Natural Language Understanding Research 
W. ~oods Bolt Beranek ~nd Inc. Newman, 
Cambridge MA 02138 
I. INTRODUCTION 
Natural language understanding has 
suddenly emerged as a central focus for many 
different disciplines. Applications are 
emerging all over the field of computer 
science in which language understanding and 
the communication of complex intentions to 
machines is a crucial part. Moreover, 
psychologists, linguists, and philosophers 
have found the models emerging from 
computational linguistics research to 
provide new stimulus and new methods for 
increasing our understanding of the process 
of human language use and the nature of 
communication. In this paper I want to 
discuss some of the methodological problems 
I see in the development of this area of 
research and some of the things which I 
think are needed in order for the field to 
be productive of real scientific insight and 
useful results. 
In order to discuss methodologies, we 
had best first understand the 
different tasks for which the methodologies 
are to be used. There are at least two 
primary interests which one can have in 
studying natural language understanding -- 
constructing intelligent machines and 
understanding human language performance. 
These two different objectives are not 
mutually exclusive, and I will attempt to 
argue that a large portion of the research 
necessary to either of them is shared by the 
other. This common portion consists of a 
pure attempt to understand the process of 
language understanding, independent of what 
device (human or machine) does the 
understanding. However, there are elements 
of the different points of view which are 
not shared, and drawing the distinction 
between objectives at the outset is, I 
think, useful. 
I would claim that both the development 
of useful mechanical devices for 
understanding language and the understanding 
of human language performance depend heavily 
on what we might call "device independent" 
language understanding theory. That is, a 
Joint study of human and machine language 
understanding, attempting to devise 
algorithms and system organizations which 
will have the functional performance of 
language understanding without specifically 
trying to model the performance aspects of 
human beings. Theoretical and empirical 
studies of this sort provide the foundations 
on which models of human language processing 
are built which are then subject to 
empirical verification. They also provide 
the "bag of tricks" out of which useful 
mechanical language understanding systems 
can be constructed. Outside the common area 
of endeavor, these two different objectives 
have different goals. For both objectives, 
however, a major component of the research 
should be to study the device independent 
language understanding problem. 
This paper is an attempt to set down my 
biases on some issues of methodology for 
constructing natural language understanding 
systems and for performing research in 
computational linguistics and language 
understanding. In it I will discuss some of 
the methods that I have found either 
effective and/or needed for performing 
useful work in the area of human and 
mechanical language understanding. 
For theoretical studies, I will argue 
strongly for a methodology which stresses 
communicable and comprehensible theories, 
with precise uses of terms and an evaluation 
of formalisms which stresses the cognitive 
efficiency of the representations of the 
formalism itself. I will attempt to cite 
several examples of the differences in 
cognitive efficiency between formalisms. 
The thrust of many of my comments will 
deal with the problems of complexity. My 
thesis is that natural language, unlike many 
physical systems is complex in that it takes 
a large number of facts, rules, or what have 
you to characterize its behavior rather than 
a small number of equations (of whatever 
theoretical sophistication or depth). It is 
relatively easy to construct a grammar or 
other characterization for a fairly small 
subset of the language (at least it is 
becoming more and more so today), but it is 
not so easy to cope with the complexity of 
the specification when one begins to put in 
the magnitude of facts of language which are 
necessary to deal with a significant 
fraction of human language performance. 
Theories for natural language understanding 
will have to deal effectively with problems 
of scale the number of facts embodied in the 
theory. 
Since this paper is largely designed to 
promote discussion, the set of issues 
covered herein makes no effort to be 
complete. My goal is to raise some issues 
for consideration and debate. 
If. A PROGRAM FOR THEORETICAL LINGUISTICS 
AND PSYCHOLOGY 
The first point that I would like to 
make is that in the pursuit of theoretical 
understanding in linguistics or 
psycholinguistics, studies will be much more 
productive if pursued in the context of 
total language understanding systems and not 
in isolation. The subdivision of the total 
process into components such as syntax and 
semantics and concentrating on one such 
component is an effective way of limiting 
scope. However, it is only justifiable if 
one has at least some reason to believe that 
his hypothesized interfaces to those other 
components are realistic (and certainly only 
if he has precisely specified those 
interfaces). One cannot expect to pursue 
some small niche of the language 
understanding process without an active 
interest in the entire process and an 
understanding of the role of his specialty 
area in that overall process. Otherwise it 
is too easy to push problems off on someone 
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else, who may not be there to catch them. 
(In particular there is considerable risk 
that the problems left for someone else may 
be insoluble due to a false assumption about 
the overall organization. Studies pursued 
under such false assumptions are likely to 
turn out worthless.) 
III. THEORETICAL AND EMPIRICAL METHODOLOGIES 
There is need in the field of natural 
language understanding for both 
theoreticians and builders of systems. 
However, neither can pursue their ends in 
isolation. As in many other fields, the 
theoretical and experimental components go 
hand in hand in advancing the understanding 
of the problem. In the case of language 
understanding, the theoretical 
investigations consist largely of 
formulation of frameworks and systems for 
expressing language understanding rules or 
facts of language and for expressing other 
types of knowledge which impact the 
understanding process. On the experimental 
side, it is necessary to take a theory which 
may appear beautifully consistent and 
logically adequate in its abstract 
consideration, and verify that when faced 
with the practical reality of implementing a 
significant portion of the facts of 
language, the formalism is capable of 
expressing all the facts and is not too 
cumbersome or inefficient for practicality. 
The day is past when one could devise a new 
grammar formalism, write a few examples in 
it, and tout its advantages without putting 
it to the test of real use. 
Today's language theoreticians must 
have a concrete appreciation of the 
mechanisms used by computerized language 
understanding systems and not merely 
training in a classical school of 
linguistics or philosophy. (On the other 
hand, they should not be ignorant of 
linguistics and philosophy either.) Some 
mechanism must be found for increasing the 
"bag of tricks" of the people who formulate 
such theories -- including people outside 
the current computational linguistics and 
artificial intelligence camps. Hopefully, 
this conference will make a beginning in 
this direction. 
IV. MODELS AND FORMALISMS 
One of the depressing methodological 
problems that currently faces the field of 
artificial intelligence and computational 
linguistics is a general tendency to use 
terms imprecisely and for many people to use 
the same term for different things and 
different terms for the same thing. This 
tendency greatly hampers communication of 
theories and results among researchers. 
One particular imprecision of terms 
that I would like to mention here is a 
confusion that frequently arises about 
models. 
One frequently hears people refer to 
the transformational grammar, model, or the 
augmented transition network grammar model, 
and asks what predictions these models make 
that can be empirically verified. However, 
when one looks carefully at what is being 
referred to as a model in these cases, we 
find not a model, but rather a formalism in 
which any of a number of models (or 
theories) can be expressed. The 
transformational grammar formalism and the 
ATN formalism may suggest hypotheses which 
can be tested, but it is only the attachment 
of some behavioral significance to some 
aspect of the formalism which gives rise to 
a testable model. 
Argume:nts for or against a model are 
whether it is true -- i.e. whether the 
predictions of the model are borne out by 
experiments. Arguments for or against a 
formalism or a methodology are its 
productiveness, economy of expression, 
suggestiveness of good models, ease of 
incorporating new features necessary to new 
hypothesized models (i. e. range of 
possible models expressible), etc. One 
needs at the very least that the formalism 
used must be capable of representing the 
correct model. But one doesn't know ahead 
of time and may never know what the correct 
model is. Hence it is desirable to have a 
formalism that can represent all conceivable 
models that could be correct. If there is a 
class of models which the formalism cannot 
account for then there should be an argument 
that no members of that class could possibly 
be correct, otherwise a formalism which 
included that class would be better (in one 
dimension). Dimensions of goodness of 
formalisms include range of possible models, 
efficiency of expression (perspicuity or 
cognitive efficiency of the formalism), 
existence of efficient simulators for the 
formalism for use in verifying the 
correctness of a model, or for finding 
inadequacies of a model, or for determining 
predictions of the model, etc. 
V. HUMAN LANGUAGE PERFORMANCE 
In order to perform good work in 
computational linguistics and in 
understanding human language performance, 
one needs to keep always in mind a good 
overview of how people use language and for 
what. Indeed, a prime focus of this 
conference is the development of such a 
overview. My own picture of the role of 
language in human behavior goes roughly like 
this: 
There is some internal representation 
of knowledge of the world which is 
prelinguistic, and we probably share most of 
it with the other higher animals -- I would 
guess we share a lot of it with cats and 
dogs, and certainly with apes and 
chimpanzees. (What differences of quality 
or quantity set us apart from these animals 
or set the chimps apart from the dogs I 
would not care to speculate. ) 
Nevertheless, it is clear that cats and dogs 
without our linguistic machinery and without 
spoken languages do manage to store and 
remember and use fairly complex pieces of 
knowledge of the world, such as how to open 
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doors, how to find their way around, where 
they left things, which dish is theirs, what 
funny sequence of sounds their owners use to 
call them (i. e. their names), and the 
significance (to them) of all sorts of 
things that go on in the world around them. 
Humans probably remember large numbers of 
such things also without specific need for 
language. We presumably have in our head 
something which is like a language in many 
respects, but which probably does not share 
the peculiar linear characteristics of 
spoken and written language (which derives 
from the temporal order imposed on speech 
and reading). 
It no doubt helps us to remember a 
larger number of things to correlate them 
with linguistic labels or a pronounceable 
sequence of sounds, and this no doubt gives 
a greater ability for abstract thought. 
However, it is doubtful that a language as 
we speak it or write it is a prerequisite 
for an organism to have what we might call 
thought. Many of the things which we "know" 
are not expressed in language, and the fact 
that finding the appropriate words to 
describe things that we understand is 
sometimes very difficult should give us a 
clue that the representation which we use in 
our heads is not a simple transcription of 
the language that we use to communicate with 
others. Rather, there are a variety of 
exposition problems which need to be solved 
in order to translate even ideas which .are 
clearly understood into a linear sequence of 
linguistic symbols which will be likely to 
arouse or create the same idea in the head 
of our listener. It seems likely then that 
the notation or whatever conventions that we 
use to store ideas and information in our 
heads is not the same as the language that 
we speak to communicate with others. 
The language that we speak and write, 
then, appears to be a device or a discipline 
evolved for the purpose of attempting to 
arouse in the head of the listener something 
similar to that which is encoded in the head 
of the speaker. 
The process of communication 
necessarily involves elements of problem 
solving. What terms does my listener know? 
What concepts can I rely on his 
understanding so that I can express what I 
want to say in terms of them? How can I 
build a specification out of these pieces 
which will cause him to construct in his 
memory the thing which I am trying to 
communicate? An account of human language 
use must deal with all of these questions. 
The above picture of the overall role 
of language in human communication may not 
be correct in many respects. Hopefully a 
consensus of this workshop will produce a 
better one. However, I am afraid that a 
complete understanding of human language use 
will have to go hand in hand with an 
understanding of the prelinguistic 
capabilities for knowledge representation 
and use which the human has. This level of 
human ability is unfortunately very 
difficult to get one s hands on since it, 
like Joe Becket's problems of intermediate 
136 
cognition, is a process which we are not 
generally aware of (since it takes place 
below the level of our conscious awareness) 
and consequently we have no direct abilities 
to see it. Rather we have to be able to 
infer its presence and its nature from 
theoretical considerations and the effects 
that it has on the overt behavior we can 
see. A methodology for working in this area 
is extremely difficult to work out. 
A principal component of such a 
methodology, I feel, should be a theoretical 
attempt to construct models which do things 
humans do and which do them well. That is, 
one should try to design intelligent 
machines which can do what humans do, and 
let the concepts that emerge from such 
designs make predictions about what 
performance one should see at the overt 
behavior interface. It is important 
however, that such studies go as far as to 
produce overt behavior which can be 
evaluated. A so called "theoretical" study 
which has no measurable performance is 
foundationless. There is no way to evaluate 
whether it is doing anything or not. In 
particular, many studies of so called 
"semantic representations" need clear 
statements of what will be done with their 
representations and how one can decide 
whether a representation is correct or 
incorrect. Without such an understanding, 
the entire exercise is one of aesthetics and 
without scientific contribution. In talking 
about semantic representations, one must be 
willing to face the questions of how the 
device knows what those representations 
mean. What events in the world will be in 
contradiction to the knowledge encoded in 
the representation and what ones will be 
consistent with it? How will a person (or a 
machine) know whether an event perceived is 
consistent with his semantic representations 
or not? How does he decide what to record 
when he perceives an event -- i. e. what 
process transforms ("transforms" is hot 
really the right word for this) an observed 
event into a linguistic description of it? 
What intervening processes take place? These 
and similar questions must be specifically 
faced. 
VI. EXPLANATORY MODELS 
The goal in trying to model human 
behavior should be to find explanatory 
models, not just descriptive models. If, 
for example, one discovers that there is a 
reaction time lag in processing certain 
types of sentences, a model which simulated 
this behavior by inserting a delay into a 
certain stage of the processing would be a 
descriptive model, whereas another model 
which took longer for processing these types 
of sentences because of some extra 
processing which they required due to the 
organization of the program would be an 
explanatory model. In my own work, the 
things which have excited me and made me 
feel that I was discovering something about 
the way the people understand language, have 
been algorithms that are motivated by 
considerations of efficiency and "good 
engineering design" for a specific task 
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which then turn out to have predictions 
which are borne out in human performance. 
An example of this is some of the work of 
Ron Kaplan and Eric Wanner using ATN 
grammars to model aspects of human 
linguistic processing. (The basic ATN 
grammar formalism was designed for 
efficiency of operation, and not 
specifically for human performance 
modeling.) When such an experiment has 
positive results, one has not only a 
description of some aspect of human 
behavior, but also a reason for the 
behavior. 
VII. COPING WITH COMPLEXITY 
A critical need for all studies in 
language understanding is effective 
mechanisms for coping with the complexity of 
the phenomenon we are trying to understand 
and explain. The models that are required 
for describing human language performance 
are more complicated than the comparatively 
simple physical phenomena in most other 
areas of science. Only the models in 
artificial intelligence and computational 
linguistics, and perhaps some kinds of 
theoretical chemistry reach the level of 
having theories which comprise thousands of 
rules (or equations) that interact in 
complicated ways. If the results of 
detailed studies of linguistic phenomena are 
to be disseminated and the field is to grow 
from the exchange of information and the 
continued accumulation of a body of known 
facts, then the facts must be capable of 
being communicated. We have then, at the 
core of the methodology of language 
understanding research, a critical need for 
some of the byproducts of our own research 
-- we need to develop effective formalisms 
for representation and for communication of 
our theories. The expression of a theory of 
language in a formal system which is 
incomprehensible or tedious to comprehend 
will contribute little to this endeavor. 
What is required then, as a fundamental tool 
for research in language understanding is a 
formalism for expressing theories of 
language (involving large numbers of 
elementary facts) in ways which are 
cognitively efficient -- i. e. which 
minimize the intellectual effort required to 
grasp and remember the functions of 
individual elements of the theory and the 
way in which they interact. 
A good example of cognitive efficiency 
of representation occurs in the 
representations of transition network 
grammars, compared with the intermediate 
stages of a transformational derivation in a 
conventional transformational grammar. It 
is well known, that humans find it easier to 
remember lists of familiar elements which 
fit together in structured ways than to 
remember dynamically varying lists of 
unfamiliar things. In a transition network 
grammar, the stages of intermediate 
processing of a sentence proceed through a 
sequence of transitions through named states 
in the grammar. Each of these states has a 
name which has mnemonic value and 
corresponds to a particular milestone or 
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landmark in the course of processing a 
sentence. A student of the language or a 
grammar designer or someone studying someone 
else's grammar can become familiar with each 
of these states as a known entity, can 
remember it by name, and become familiar 
with a variety of information associated 
with that state -- such as what kinds of 
linguistic constructions preceeded it, what 
constructions to expect to the right, 
prosodic cues which can be expected to 
accompany it, potential ambiguities and 
disambiguation strategies, etc. The 
corresponding intermediate stages of a 
transformational grammar go through a 
sequence of intermediate phrase marke~s 
which do not exist ahead of time, are not 
named, have no mnemonic value, are 
constructed dynamically during a parsing, 
and in general provide none of the above 
mentioned useful cognitive aids to the 
student of the grammar. 
Similarly, the information remembered 
during the course of a parsing with an ATN 
is stored in named registers, again with 
mnemonic value, while the corresponding 
information in a transformational 
intermediate structure is indicated solely 
by positional information in the 
intermediate tree structure with no such 
mnemonic aid, with an attendant difficulty 
for memory, and with the added difficulty 
that it is possible to construct a structure 
accidentally which matches the input pattern 
of a rule that one did not intend it to 
activate. The chance of doing this 
accidentally with a mnemonically named 
register or condition is negligible. 
Many other techniques for expressing 
complicated systems with cognitive 
efficiency are being developed by 
programmers in sophisticated languages such 
as INTERLISP, where some programmers are 
adopting styles of programming which make 
the understanding of the program by human 
programmers and students easier. A major 
technique of these programming styles from 
the standpoint of cognitive efficiency is 
the use of a hierarchy of subroutines with 
specified function and mnemonic names to 
produce program structures which match 
closely the human conceptual model of what 
the program is doing. In such systems, one 
can verify the successful operation of an 
algorithm by a method called recursion 
induction, which effectively says that if 
all of the subroutines do the right thing, 
then the main routine will also do the right 
thing. If one is sufficiently systematic 
and careful in his programming style, then 
the assurance that each level of the program 
does the right thing can be guaranteed by 
inspection and the chances of writing 
programs with hidden bugs or complicated 
programs whose function cannot be easily 
understood is greatly reduced. 
As an example, consider a technique 
which I use extensively in my own 
programming in LISP. Suppose that I have a 
data object called a configuration which is 
represented as a list of 5 elements and the 
second element of the list is the state of 
the configuration. It is a simple matter of 
programming discipline to extract the state 
name from such a data object with a function 
called CONFIG.STATE rather than the LISP 
function CADR, with the result that the 
program is almost self documenting instead 
of incomprehensible. It is easy in 
INTERLISP to define the two functions 
identically and to cause them to compile 
identically so that no cost in running time 
is necessitated by such programming 
techniques. (In my case I have a LISP 
function called EQUIVALENCE which takes care 
of all the details if I simply call 
(EQUIVALENCE (CONFIG.STATE CADDR)).) 
Recently, new features have been added to 
INTERLISP which further facilitate such 
programming conventions by providing the 
user with a generalized facility for record 
naming and field extraction. 
Another example of the principle of 
cognitive efficiency arises in the now 
famous go-to controversy of the programming 
language theorists. One school argues that 
one should program in a structural 
discipline which makes go-to instructions 
unnecessary and that such a discipline 
should be forced on a programmer because the 
code he will write under such a discipline 
will be better. This extreme point of view 
is presumably in contrast to the situation 
in the language FORTRAN where one can handle 
branching only by "naming" each of the 
branches with (unmnemonic) numeric labels 
and specifying go-to instructions in terms 
of such labels. However, I would argue that 
in many other situations, with a language 
which permits mnemonic labels, a programmer 
can insert a go-to instruction for the same 
kinds of reasons that he creates many 
subroutines -- i.e., there is a significant 
chunk of operation which in his mind is a 
unit (for which he has or can coin a name) 
and which he would like to represent in his 
code in a way that will enable him to read 
portions of the code at a level of detail 
which is cognitively efficient. When go-to 
instructions are used in this way, they have 
the same value that the ability to write 
subroutines provides (not only efficiency of 
writing a given portion of code once while 
being able to enter it for execution from 
several places, but also the cognitive 
efficiency of being able to ignore details 
of how some process operates by referring to 
it by name or label in situations where it 
is the purpose or goal of a procedure or 
block of code which is important and not the 
details). 
VIII. THE NEED FOR A COMPREHENSIBLE 
FRAMEWORK 
Not only must the individual rules of a 
complex system be comprehensible to the 
system designer and the student, but also 
the control framework into which these rules 
fit must be understood. Again, there is a 
principle of cognitive efficiency in 
operation. A control framework which is 
simple to explain and easily remembered by 
the student of the system as he studies it, 
is far preferable to one which constantly 
misleads the student into thinking that 
something happens in one way when it 
actually happens differently or not at all. 
One cannot write rules for a system when he 
is not sure how it will apply the rules or 
when. Languages which take away from the 
programmer the burden of specifying the 
details of control structure should not also 
take away his ability to easily understand 
and forsee what will happen in response to 
his rules. 
IX. COGNITIVE EFFICIENCY IN GRAMMARS 
One of the dilemmas of the field of 
computational linguistics has been the 
difficulty of evaluating the quality of a 
grammar which someone has written. What is 
the scope of grammatical phenomena which it 
covers? It is one thing to say that a 
grammar handles questions, imperatives, 
comparatives, adverbs, etc. It is another 
thing to discover that what this means is 
that certain yes/no and simple wh- questions 
are handled, that a certain class of 
comparatives (the easy ones) are handled, 
and that only single word adverbs before or 
after the main verb are handled. A list of 
phenomena supposedly dealt with is obviously 
not sufficient. 
A common attempt to specify the class 
of sentences accepted by a grammar is to 
list a sample set of the sentences covered. 
This tends to give a feeling for what the 
grammar can handle, but depending on the 
scrupulousness of the author in pointing out 
the things that his grammar doesn't handle 
(assuming he realizes what it doesn't 
handle) it is very easy for the reader to 
overgeneralize the range actually handled. 
When the author lists several examples of 
different kinds of comparatives, how does 
the reader decide whether all possibilities 
are handled or Just those cases that are 
listed. The problem is that what one wants 
is a precise, compact, and comprehensible 
representation of exactly the class of 
sentences which are acceptable and how they 
are handled. But, notice that to the extent 
that such a specification is realizable, 
that is exactly what a grammar should be. 
Hence, the thing that is needed is a 
formalism for grammar specification which is 
precise, compact, and comprehensible to a 
human grammar designer. In short, we need a 
formalism for grammar specification which is 
cognitively efficient -- enough so that a 
grammarian can tell by inspection of the 
grammar whether a sentence is acceptable or 
not. While this may not be realizable to 
this extent, it seems to focus on the 
hopelessness of attempting to find some 
other specification of what a grammar does 
which will somehow be clearer than the 
grammar itself. Instead, it shifts the 
emphasis to making the grammar formalism 
sufficiently perspicuous that one can study 
it and understand it directly. 
The only other method I know of at the 
present to obtain answers to specific 
questions about what a grammar does is to 
get your hands on the system and probe it 
with your theories of what it handles and 
what it doesn't. This has its own 
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disadvantages in the other direction, since 
it is indeed possible for a sentence to fail 
for a trivial reason that is a simpl e bug in 
a program and not because the grammar is 
incorrect or the theory is inadequate. 
Moreover, it is almost impossible for anyone 
but the designer and implementer of the 
system to tell whether it is a simple bug or 
a real conceptual difficulty and one 
certainly can't simply take on faith a 
statement of "Oh that's just a bug." 
However, I think that it is inevitable that 
natural language grammars will reach a level 
of complexity, no matter how perspicuous one 
makes the grammar, where computer aid in 
checking out theories and finding out what 
is or is not handled is an essential tool. 
Thisdoes not obviate the need for cognitive 
efficiency, however. 
To make the matter more complicated, in 
many systems, now, the syntactic component 
is not separable from the semantics and 
pragmatics of the system so that a sentence 
can fail to be handled correctly not only 
due to incorrect syntax (i. e. the grammar 
does not match the reality of what people 
say) but also due to concepts which the 
system does not know or things which the 
system finds inappropriate to the context. 
For such systems, it is almost impossible to 
judge the capability of the individual 
components of the system in any objective 
and non idiosyncratic terms. Each system is 
unique in the scope of what it is trying to 
do and finding any equivalent grounds on 
which to compare two of them is difficult if 
not impossible. The ability to understand 
the formalism in which the author expresses 
his theory and presents it to the world is 
critical. 
comprehension as well as mechanical 
implementation. In addition, I have 
discussed the need to perform research in 
the specialized areas of language 
understanding within the framework of a 
global picture of the entire language 
understanding process. I have called for 
more care in the precise use of terms and 
the use where possible of accepted existing 
terms rather than inventing unnecessary new 
ones. I have also stressed the necessity 
that models must produce some overt behavior 
which can be evaluated, and have noted the 
desirability of finding explanatory models 
rather than mere descriptive models if one 
is really to produce an understanding of the 
language understanding process. I hope that 
the paper will serve as a useful basis for 
discussion. 
REFERENCES 
Becker, J.D. "An Information Processing 
Model of Intermediate-Level Cognition," 
Memo AI-119, Stanford Artificial 
Intelligence Project, Stanford 
University, Stanford, Calif., May, 1970. 
International Joint Conference on Artificial 
Intelligence, London, England, 
September, 1971. 
Woods, W.A. "Transition Network Grammars 
for Natural Language Analysis," Comm. 
ACM, Vol 13, No. 10, (October, 1970). 
X. CONCLUSION 
In conclusion, the major thrust of this 
paper has been to stress the complexity of 
scale which must be dealt with in 
representing theories of natural language 
understanding and especially in 
communicating them to other people. My 
major methodological weapon against this 
complexity, is to develop specification 
languages and notations which are 
cognitively efficient in the sense that they 
minimize the human intellectual effort 
necessary to understand, remember, design, 
and use such formalisms. We should strive 
for notations that can be used to publish 
grammars, semantic specifications, and 
knowledge bases in a form that one can 
realistically expect other people to read 
and understand. Simple things such as 
naming functions with names that will invoke 
the correct concept in the head of the 
person studying the formalism (rather than a 
clever name the author fancies, or the first 
thing he happened to name it, or the name it 
used to have when he used it for something 
else, etc.) can make an enormous difference 
in the cognitive efficiency of a formalism. 
In short, I am making a plea for making the 
specification language used for theory 
development in natural language 
understanding be a communication language 
intended and engineered for human 
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