SOME I33UE3 IH P&RSING AHD NATURAL LINGUAGE UNDERSTANDING 
Robert J. Bobrow 
Bolt Beranek and ~ewman Inc. 
Bonnie L. Webber 
Department of Computer & Information Science 
University of Pennsylvania 
Lan&ua~e is a system for ancodln~ and 
trans~tttlnK ideas. A theory that seeks to 
explain llnKulstlc phenomena in terme of this 
fact is a fun~t~1 theory. One that does not 
• £sses the point. \[10\] 
PREAMBLE 
Our response to the questions posed to this panel is 
influenced by a number of beliefs (or biasesl) which we 
have developed in the course of building and analyzin~ 
the operation of several natural language understanding 
(NLU) systems. \[I, 2, 3, 12\] While the emphasis of the 
panel i~ on parslnK, we feel that the recovery of the 
syntactic structure of a natural lan~unKe utterance 
must be viewed as part of a larger process of 
reeoverlnK the meaning, intentions and goals underlying 
its generation. Hence it is inappropriate to consider 
designing or evaluatln~ natural language parsers or 
Erem,~ra without taking into account the architecture 
of the whole ~LU system of which they're a part. I This 
is the premise from which Our beliefs arise, beliefs 
which concern two thinks: 
o the distribution of various types of 
knowledge, in particular syntactic knowledge, 
amonK the modules of an NLU system 
o the information and control Flow emonK those 
modules. 
As to the first belief, in the HLU systems we have 
worked on, most syntactic information is localized in a 
"syntactic module", although that module does not 
produce a rallied data structure representing the 
syntactlo description of an utterance. Thus, if 
"parslnK" is taken as requlrln~ the production of such 
a rallied structure, then we do not believe in its 
necessity. However we do believe in the existence of a 
module which provides syntactic information to those 
other parts of the system whose decisions ride on it. 
As to the second belief, we feel that syntax, semantics 
and prattles effectively constitute parallel but 
interacting processors, and that information such as 
local syntactic relations is determined by Joint 
decisions -monk them. Our experience shows that with 
mlnir"al loss of efficiency, one can design these 
processors to interface cleanly with one another, so as 
to allow independent design, implementatlon and 
modification. We spell out these beliefs in slightly 
more detail below, and at greater length in \[~\]. 
1We are not claiming that the only factors shaping a 
parser or a gr~-mar, beyond syntaotlo conslderatlofls, 
are thlrLKs llke meanlng, intention, etc. There are 
clearly mechanical and memory factors, aa well an 
laziness - a speoXer's penchant for trylnK to get away 
with the mdniEal level of effort needed to accomplish 
the taskf 
97 
The Comoutatiom~l Persneetive 
The first set of question~ to this panel concern the 
computational perspective, and the useful purposes 
served by distinguishing parsing from interpretation. 
We believe that syntactic knowledge plays an important 
role in NLU. In particular, we believe that there is a 
significant type of utterance description that can be 
determined on purely syntactic grounds 2, albeit not 
necessarily uniquely. This description can be used to 
guide semantic and discourse level structure recovery 
processes such as interpretation, anaphoric resolution, 
focus tracking, given/new distinctions, ellipsis 
resolution, etc. in a manner that is independent of the 
lexical and conceptual content of the utterance. There 
are several advantages to factoring out such knowledge 
from the re,~-~nder of the NLU system and prowlding a 
• syntactic module" whose interactions with the rest of 
the system provide information on the syntactic 
structure of an utterance. The first advantage is to 
simplify system building, an we know fl-om 
experience \[I, 2, 3, 4, 5, 12\]. Once the pattern of 
communication between processors is settled, it is 
easier to attach a new semnntlcs to the hooks already 
provided in the Kr~,mar than to build a new semantic 
processor. In addition, because each module ban only 
to consider a portion of the constraints implicit in 
the data (e.g. syntactic constraints, semantic 
constraints and discourse context), each module can be 
designed to optimize its own processing and provide an 
efficient system. 
The panel has also been charged wlth _ ~oslderlng 
paa'allel processing as a challenge to its views on 
parsing. Thls touches on our beliefs about the 
Interaction among the modules that comprise the HLU 
system. To respond to this issue, we first want to 
dlstlngulsh between two types of parallelism: one, in 
which many instances of the same thin6 are done at once ~ 
(an in an array of parallel adders-) and another, in 
which the many thinks done slmul~aneously can be 
different. Supporting this latter type of parallelism 
doesn*t change our view of parsing, but rather 
underlies it. We believe that the Interconnected 
processes involved in NLU must support a banjo 
o~eratinK pri~iple that Herman and Bobrow \[14\] have 
called "The Principle of Continually Available Output":, 
(CAO). This states that the Interactlng processes muat~ 
benin to provide output over a wide range of resource 
allocations, even before their analyses are complete, 
and even before all input data is available. We take 
this position for two rensons: one, it facilitates 
computational efficiency, and two, it seems to be 
closer to human parsing ~rocesses (a point which we 
will get to in answerlnK the next question). 
The requirement that syntactic analysis, semantic 
interpretation and discourse processlng must be able to 
operate in (pseudo-)parallel, obeying the CAO 
2that is, solely on the baa£s of syntactic 
categories/features and ordering Information 
principle, has sparked our interest in the design of 
calrs of processes which can pass forward and backward 
unet~Ll In/ormatlon/advlce/questlons as soon as 
possible. The added potential for interaction of such 
processors can increase the capability and efficiency 
of the overall HLU process. Thus, for example, if the 
syntactic module makes its intermediate decisions 
available to semantics and~or pragmatlcs, then those 
processors can evaluate those decisions, guide syntax's 
future behavior and, in addition, develop in parallel 
their own analyses. Having sent on its latest 
assertlon/advlce/question, whether syntax then decides 
to continue on with something else or walt for a 
response will depend on the particular kind of message 
sent. Thus, the parsers and grammars that concern us 
are ones able to work with other appropriately designed 
compoconts to support CAO. While the equipment we are 
USing to implement and test our ideas is serial, we 
take very seriously the notion of parallelism. 
Finally under the heading of "Computational 
Perspective", we are anked about what might motivate 
our trying to make parsing procedures simulate what we 
suspect human parsing processes to be like. One 
motivation for us is the belief that natural language 
is so tuned to the part extraordinary, part banal 
cognitive capabilities of human beings that only by 
simulating human parsing processes can we cover all and 
only the language phenomena that we are called upon to 
process. A particular (extraordinary) aspect of hu~an 
cognitive (and hence, parsing) behavior that we want to 
explore and eventually simulate is people's ability to 
respond even under degraded data or resource 
limitations. There are examples of listeners 
initiating reasonable responses to an utterance even 
before the utterance is complete, and in some case even 
before a complete syntactic unit has been heard. 
Simultaneous translation is ode notable example \[8\], 
and another is provided by the performance of subjects 
in a verbally guided assembly task reported by P. Cohen 
\[6\]. Such an ability to produce output before all 
input data is available (or before enough processing 
resources have been made available to produce the best 
possible response) is what led Norman and Bobrow to 
formulate their CAO Principle. Our interest is in 
architectures for NLU systems which support CAO and in 
• search strategies through such architectures for an 
opti~"l interpretation. 
The LimnLiStlC ~rs~etlve 
We have been asked to comment on legitimate inferences 
about human linsulstic competence and performance that 
we can draw from our experiences with mechanical 
parsing of formal grammar. Our response is that 
whatever parsing is for natural languages, it is still 
only part of a larger process. Just because we know 
what parsing is in formal language systems, we do not 
secsssarily know what role it plays is in the context 
Of total communication. Simply put, formal notions of 
parsing underconstraln the goals of the syntactic 
component of an NLU system. Efficiency meanures, based 
on the resources required for generation of one or all 
complete parses for s sentence, without semantic or 
pra~e~-tlc Intera~tlon, do not secessarily specify 
desirable properties of a natural language syntactic 
analysis component. 
As for whether the efficiency of parsing algorlthm~ for 
CF or regular grammars suggest that the core of NL 
igremmars la CF or regular, we want to dlstlngulsh that 
part of perception (and hence, syntactic analysis) 
which groups the stimulus into recognizable units from 
that part which fills in gaps in in/ormatlon 
(inferentially) on the baals of such groups. Results 
in CF grammar theory says that grouping is not best 
dose purely bottom-up, that there are advantages to 
t ~ 
uslng predictive mechanlsms a~ well \[9, 7\]. Thls 
snggests two things for parsing natural language: 
I. There is a level of evidence and a process 
for using it that is worEing to suggest 
groups. 
2. There is another filtering, inferenclng 
mechanism that maEes predictions and 
diagnoses on the basis of those groups. 
It is possible that the grouping mechanism may make use 
of strategies applicable to CF parsing, such as well- 
formed substrlng tables or charts, without requiring 
the overall language specification be CF. In our 
current RUS/PSI-ELONE system, grouping is a function of 
the syntactic module: its output consists of suggested 
groupings. These snggestlons may be at abstract, 
specific or disjunctive. For example, an abstract 
description m~ht be "this is the head of an NP, 
everything to its left is a pre-modifler". Here there 
is co comment about exactly how these pre-modlflers 
group. A disjunctive description would consist of an 
explicit enumeration of all the possibilities at some 
point (e.g., "this is either a time prepositional 
phrase (PP) or an agentive PP or a locative PP, etc."). 
Disjunctive descriptions allow us to prune. 
possibilities via cane a~alysls. 
In short, we believe in using as much evidence from 
formal systemn a~ seems understandable and reasonable, 
to constrain what the system should be doing. 
The Interaetlons 
Finally, we have been asked about the nature of the 
relationship between a gr~mar and a procedure for 
applying it. On the systems building side, cur feeling 
is that while one should be able to take a grammar and 
convert it to a recognition or generation 
procedure \[I0\], it is likely that such procedures will 
embody a whole set of principles that are control 
structure related, and not part of the grammar. For 
example, a gr',-mr seed not specify in what order to 
look for thln~s or in what order decisions should be 
made. Thus, one may not be able to reconstruct the 
grammar unlcuelv from a procedure for applying it. 
On the other hand, on the b,m- parsing side, we 
definitely feel that natural language is strongly tuned 
to both people's means of production and their means of 
recognition, and that principles llke MnDonalds ' 
Zndeliblllty Pr"Inoiple \[13\] or Marcus' Determinism 
Hypothesis \[11\] shape what are (and are not) seen an 
sentences of the language. 
REFERENCES 
I. Bobrow, R. J. The RUS System. BEN Report 3878, 
Bolt Beranek and Rewman Inc., 1978. 
2. Bobrow, R. J. & Webber, B. L. PSI-ELONE- Parsing 
and Semantic Interpretation in the BBN Natural Language 
Understanding System. CSCSI/C~EI0 Annual Conference, 
CSC3I/CSEIO, 1980. 
3. Bobrow, R. J. & Webber, B. L. Knowledge 
Representation for Syntactic/Semantic Processing. 
Proceedings of The First Annual National Conference on 
Artiticial Intelligence, American Association for 
Artificial Intelligence, 1980. 
98 
~. Bobrow, R.J. & Webber, B.L. Parsing and Semantic 
Interpretation as an Incremental Recognition Process. 
Proceedings of a Symposium on Modelling Human Parsing 
Strategies, Center for Cognitive Science, University o\[ 
Texas, Austin TZ, 1981. 
5. Bobrow, R.J. & Webber, B.L. Systems Considerations 
for Search by Cooperating Processes: Providing 
Continually Ava/lable Output. Proceedings of the Sixth 
IJCAI, International Joint Conference on Artificial 
Intelligenoe, 1981. 
6. Cohen, P. personal communication, videotape of 
experimental task 
7. Eau-ley, J. An efficient context-fl'ee parsing 
algorithm. ~ of the ACM /~ (February 
1970), 9~',- 102. 
8. Gold~an-Eisler, F. Psyohologloal Heohanisms of 
Speech Produotlon as SSudled through the Analysis of 
Simultaneous Translation. In B. Butterworth, Ed., 
Lan~rn~e Production, Aoademlc Press, 1980. 
9. Graham, S., Harrison, M. and Ruzzo, W. An Improved 
Context-Free Recognizer. ACM ~ on 
Pnom,-mm4,~ Lana~es and Systems (July 1980), "16- 
@63. 
10. Kay, M. An Algorithm for Compiling Parsing Tables 
f~om a Grammar. Prooeedings of a Symposium on 
Modelling Human Parsing Strate~Les, Center for 
Cognitive Science, University of Texas, Austin TX, 
1981. 
11. MaPcus, M. A Theory of .qvntactic ~ for 
Mat~al Lan~e. MIT Press, 1980. 
12. Mark, W. S. & Barton, G. E. The RUSGrammar 
Parsing System. GMR 32"3, General Motors Research 
Laboratories, 1980. 
13. MoDonald, D. ???. Ph.D. Th., Massachusetts 
Institute o£ Technology, 1980. 
I,. ~orman, D. & Bobrow, D. On Data-ii~ted and 
Resource-llmlted ProoesSes. CSL7,-2, Xerox PARC, Msy, 
197,. 
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