5. Sublanguages 
Richard Kittredge, Chairperson 
Universitd de Montreal 
Montreal, Quebec PQH3C3J7 
Panelists 
Joan Bachenko, Naval Research Laboratory 
Ralph Grishman, New York University 
Donald E. Walker, SRI International 
Ralph Weischedel, University of Delaware 
5.1 Why Are Sublanguages Important for Applied 
Computational Lingustics? 
Four of the five panels at this workshop are assess- 
ing the perspectives in applied computational linguis- 
tics for four important problem areas: natural-language 
interfaces, machine translation, text generation, and 
concept extraction. For each of these areas, it is as- 
sumed that any applied system will be oriented toward 
the particular variety of natural language associated 
with a single knowledge domain. This follows from 
the now widely accepted fact that such systems require 
rather tight, primarily semantic, constraints to obtain a 
correct analysis, and that such constraints can at pres- 
ent be stated only for sublanguages, not for the lan- 
guage as a whole. Although a practical system may 
well have components that are designed to accommo- 
date the whole language, it must also anticipate the 
particular syntactic, lexical, semantic, and discourse 
properties of the sublanguage in which it will operate. 
Research into the linguistic structure of weather 
reports, medical records, and aircraft maintenance 
manuals has led to specialized grammars for the sub- 
languages of these domains. Central to each sublan- 
guage grammar is a statement of the functionally simi- 
lar word classes and the co-occurrence restrictions 
among these classes. When a parser, generator, or 
translation system incorporates such a precise linguis- 
tic description, it becomes not only more efficient but 
also capable of discriminating between sentences (and 
texts) that are appropriate to the domain and those 
that are grammatical but inappropriate. In addition, 
the word classes used in the grammar, and the hierar- 
chies relating these classes, are an important part of 
the knowledge structure for the domain. 
5.2 How Do Sublanguages Arise? 
When natural language is used in a sufficiently 
restricted setting, we may be justified in calling the 
resultant forms a sublanguage. Although there is no 
generally accepted definition of this term. Several 
factors are usually present when the subset of a natu- 
ral language is restricted enough for efficient semantic 
processing. 
• Restricted domain of reference. The set of objects 
and relations to which the linguistic expressions 
refer is relatively small. 
• Restricted purpose and orientation. The relation- 
ships among the participants in the linguistic ex- 
change are of a particular type and the purpose of 
the exchange is oriented towards certain goals. 
• Restricted mode of communication. Communica- 
tion may be spoken or written, but there are 
constraints on the form of expression, which may 
include "bandwidth" limitations. Compressed (or 
telegraphic) language forms may reflect the time 
and space constraints of certain communication 
modes. 
• Community of participants sharing specialized 
knowledge. The best canonical examples of sub- 
languages are those for which there exists an iden- 
tifiable community of users who share specialized 
knowledge and who communicate under restrictions 
of domain, purpose, and mode by using the sublan- 
guage. These participants enforce the special pat- 
terns of usage and ensure the coherence and com- 
pleteness of the sublanguage as a linguistic system. 
5.3 Constraints and Extensions in the Grammar of a 
Sublanguage 
A typical sublanguage makes use of only a part of 
the language's lexical, morphological, syntactic, seman- 
tic, and discourse structures. These restrictions on its 
grammar, once detected and encoded in the form of 
rules, can be exploited during automatic processing by 
greatly reducing the number of possibilities to be con- 
sidered. A sublanguage may also exhibit structures 
(and, hence, rules) that are not normally regarded as 
part of the standard language. In the most general 
case, then, a sublanguage grammar intersects, but is 
not contained in, the grammar of the general or stand- 
ard language from which it derives. 
Some of the typical constraints and extensions 
found in each component of a sublanguage grammar 
are given below, along with reference to recognized 
techniques for describing the constraints and for iden- 
tifying them in a corpus of texts, when appropriate. In 
American Journal of Computational Linguistics, Volume 8, Number 2, April-June 1982 79 
Richard Kittredge Sublanguages 
addition, we mention a number of mechanisms for 
capturing these constraints for the purposes of com- 
puter processing. 
5.3.1 Lexical and Morphological Characteristics 
The most obvious feature of a sublanguage is its 
specialized lexicon. Not only is the set of word forms 
(and their possible meanings) highly restricted, but the 
productive word-formation rules may be of a particular 
kind, sometimes unique to the sublanguage or to a 
family of related sublanguages. Texts in medicine and 
pharmacology, for example, may contain a rich variety 
of names for diseases and drugs, which are constructed 
using characteristic affixes. Military sublanguages 
make frequent use of acronyms which obey describa- 
ble rules of noun phrase formation in the grammar. 
Many sublanguages employ symbolic expressions (e.g., 
mathematics) or abbreviations which can be shown to 
have their own morphological characteristics. 
Techniques for identifying the special morphology 
of sublanguage terms are readily available from lin- 
guistics. In cases where the lexicon is large, the de- 
signer of a computational system may find it profitable 
to include word-formation rules in a special processing 
phase. 
5.3.2 Syntactic Characteristics 
Early work on restricted language has shown that 
the syntactic description of a naturally occurring sub- 
language may differ significantly from that of a 
unrestricted language. In the highly constrained style 
of weather bulletins, there is little resemblance be- 
tween the syntactic structure of telegraphic forecasts 
and that of general language. The syntactic rules are 
essentially those of a semantic grammar (Burton 
1976). The TAUM-METEO system (Chevalier et al. 
1978) for translating Canadian weather bulletins is 
based on a grammar arrived at through a distributional 
analysis of a large corpus of these texts. In less ster- 
eotyped sublanguages such as medical records, there 
may be both elliptical sentence forms and their full- 
sentence paraphrases in the sublanguage. Thus the 
NYU system for extracting formatted data from medi- 
cal records (Sager 1978, 1981) must include in its 
parser special rules for elliptical forms as well as more 
general syntactic rules for the full forms. 
Most sublanguages of English observe the syntactic 
patterns of standard English but may differ markedly 
in the frequency of usage of various constructions. 
For example, many of the question forms, stylistic 
inversions, and exclamatives of conversational English 
are totally absent from technical literature (Lehrberger 
1981). Grammars for processing technical language 
may therefore delete the corresponding production 
rules for analysis in technical domains. On the other 
hand, some sublanguages may use syntactic construc- 
tions unknown in the general language, in which case 
the appropriate productions must be included in the 
sublanguage grammar. 
Even when certain grammatical constructions can- 
not be ruled out of the grammar, they may be of such 
high or low frequency in the sublanguage that this fact 
can be used to reorganize the order in which rules are 
tried or to change the preference weighting assigned to 
competing syntactic analyses. 
5.3.3 Semantic Constraints 
The restricted domain of reference of a sublan- 
guage is mirrored in the way words are used with re- 
spect to one another. A distributional analysis of 
word co-occurrences in a large corpus of texts (Harris 
1963; Hirschman, Grishman, and Sager 1975) allows a 
computational linguist to group words into equivalence 
classes and to describe the occurring sentences in 
terms of these classes. Computational systems which 
use the semantic grammar approach (Burton 1976) 
state the syntax directly in terms of such distributional 
classes, which are relevant for the semantic or func- 
tional distinctions to which the system s sensitive. 
Collapsing syntax and semantics in this way is useful 
for small sublanguages (Hendrix et al. 1978; Epstein 
and Walker 1978), but there is the disadvantage that 
the grammar has no generality and a new one has to 
be written for each new sublanguage. Though one 
argument for semantic grammars has been that they 
are computationally more efficient, recent experiments 
in which a semantic grammar was compared with a 
linguistically motivated grammar for the same database 
demonstrated that the latter could be just as efficient 
(cf. Sagalowicz 1980). 
In more complex sublanguages it is usually neces- 
sary to maintain traditional syntactic categories, and 
hence to couch parsing rules in terms of these categor- 
ies. In this case, semantic constraints in the form of 
selectional restrictions can be applied either during or 
directly after parsing to eliminate those syntactic ana- 
lyses that give meanings impossible in the sublanguage 
(Sager and Grishman 1975, Sager 1981, Robinson 
1,980). 
Most sublanguage texts also have larger informa- 
tion structures beyond the word-class co-occurrences 
of single sentences. An analysis of the information 
formats of medical records (Hirschman and Sager 
1981) has been carried out for the purpose of infor- 
mation retrieval. Frame-like structures may also be 
employed to recognize and extract larger information 
components (e.g., Bobrow et al. 1977, Schank et al. 
1980). 
A number of techniques are being developed for 
the specification and representation of semantic struc- 
tures that can extend beyond the sentence unit. One 
80 American Journal of Computational Linguistics, Volume 8, Number 2, April-June 1982 
Richard Kittredge Sublanguages 
entails the assignment of propositional structures to 
text passages (Walker and Hobbs 1981). Domain and 
protocol analysis (Davis 1977, Newell and Simon 
1972, Malhotra 1975) provide techniques for hypoth- 
esizing facts and inference rules appropriate for se- 
mantic analysis and reasoning procedures. Knowledge 
acquisition procedures (Davis 1977, Haas and Hendrix 
1980, Rychener 1980), now under investigation, could 
significantly aid in the building of semantic and infer- 
ence components. 
5.3.4 Discourse Considerations 
Recent research has shown that the way in which 
sentences are strung together to form coherent text 
can vary considerably from one sublanguage to anoth- 
er. In addition to differences in discourse-level se- 
mantic structures (see 5.3.3), separate sublanguages 
may make different use of a language's linguistic 
means of textual cohesion. In view of the considera- 
ble attention given to anaphora in the literature of 
computational linguistics, it is worthwhile to note that 
certain technical sublanguages contain no occurrences 
of anaphoric pronouns, while others make use of spe- 
cial anaphoric devices (Kittredge 1981). Even when a 
technical sublanguage uses pronominal anaphora, it 
often appears that the sublanguage effectively restricts 
it to cases where the antecedent noun phrase occurs in 
the preceding sentence or even in an earlier clause in 
the same sentence. Needless to say, the strategy em- 
ployed for establishing co-reference in a sublanguage 
must therefore take into account the behavior of each 
anaphoric device in that same sublanguage. In many 
cases, a far simpler algorithm can be used than would 
be necessary for unrestricted language. In any given 
language, the semantic coherence and grammatical 
cohesion of a text can be signalled by a variety of 
linking devices. From a language's inventory of de- 
vices, each sublanguage seems to make a rather dis- 
tinctive and limited selection. Stock market reports 
avoid repetition of the same verb in successive sen- 
tences, using synonyms instead, whereas technical 
manuals apparently avoid synonymy at the expense of 
lexical repetition (Kittredge 1981). The use of tense 
or tense variation may also fit a distinctive pattern. 
All such tendencies, whether probabilistic or absolute, 
may be exploited during the design of optimized sub- 
language processing systems. 
5.4 Factors Defining Suitable Candidate Applications 
The sublanguage approach to language processing 
may not be appropriate to all varieties of restricted 
language or all types of application. It may only be 
profitable where there exists an established group of 
users who help to identify and define the knowledge 
domain. In addition, the domain should be relatively 
well-defined and internally consistent. The most tract- 
able sublanguages from the computational point of 
view are those that present a simple discourse struc- 
ture. Finally, each application should be one in which 
the computer is an appropriate medium of communica- 
tion or processing (e.g., spoken sublanguages or ones 
for which permanent records would not or should not 
be kept may not be appropriate). 
In practical applications where economic considera- 
tions are decisive, one must also take into account the 
time and cost of studying the linguistic properties in a 
sufficiently large and representative sample of the 
sublanguage and of creating and programming the 
sublanguage-specific dictionary and grammar rules. 
There is reason to believe that sublanguages that are 
semantically and pragmatically near-neighbors are 
similar in their grammatical properties, so that a better 
understanding of language form and function will 
make the description of new sublanguages easier and 
more predictable. 
5.5 Maturing Areas of Research Relevant to the 
Sublanguage Approach 
A successful general approach to sublanguage proc- 
essing in a wide variety of domains will depend on 
advances in a number of research areas, some of which 
are maturing rapidly. Empirical work on knowledge 
structures (Bobrow et al. 1977, Mark 1980, Robinson 
et al. 1980) and on mechanisms of focus (Grosz 1977, 
1981) is relevant to a proper treatment of sublanguage 
specific features of discourse and semantic structure. 
Techniques of using precise selectional restrictions 
for sublanguages have been implemented (Burton 
1976) as have those for extracting formatted informa- 
tion from fairly stereotyped sublanguages (Sager 
1978). A new technique for developing transportable 
systems for natural-language interfaces to databases 
(Hendrix and Lewis 1981) elicits from the user a lan- 
guage for querying the contents at the same time that 
information about the domain is being entered. This 
approach is being extended to provide a more sophisti- 
cated system that is not limited to formatted databases 
but entails translation into a set of well-formed formu- 
las in a many-sorted first-order logic (Haas and Hen- 
drix 1980). Recent work on treating departures from 
grammaticality (Sondheimer and Weischedel 1980, 
Hayes and Mouradian 1980, McKeown 1980, Kwasny 
and Sondheimer 1981, Miller et al. 1981) can be use 
in handling specialized language that deviates syntacti- 
cally from the standard language. Devices for design- 
ing more "friendly" systems, such as the work on 
graceful interaction (Kaplan 1978, Hayes and Reddy 
1979, Weischedel and Sondheimer 1981) are relevant 
to the question of relating sublanguage-specific phe- 
nomena to those of the whole language. 
American Journal of Computational Linguistics, Volume 8, Number 2, April-June 1982 81 
Richard Kittredge Sublanguages 
5.6 Promising New Research Areas 
A number of new or even underdeveloped research 
areas will certainly prove important for work on sub- 
language. We expect that further research on syntac- 
tic variation will yield a more unified framework for 
the description of sublanguage word and phrase struc- 
ture. Work in pragmatics, such as the recent computa- 
tional modeling of speech acts, will intersect with in- 
vestigations into sublanguages where social or legal 
dimensions are important. As we accumulate experi- 
ence in semantic processing over a number of specialty 
areas, we will be able to identify more and more 
sharply the important parameters for assessing the 
computational tractability of any given sublanguage. 
This experience will also nourish a distinct area which 
has both theoretical and practical aspects: the prob- 
lem of relating sublanguages (and their grammars) to 
the standard language (and its grammar). The pre- 
liminary efforts at building up a taxonomy and typolo- 
gy of sublanguages are aimed in this direction. 
There is already an identifiable movement towards 
codifying and teaching language for specific purposes. 
For some applications it is possible to take naturally 
occurring sublanguages and slightly regularize them so 
that strong tendencies are promoted to norms for com- 
municating in the subfield. Attempts in this direction 
have occurred in the stylistic guidelines now used for 
writing weather reports and aircraft maintenance man- 
uals. A serious scientific approach to this "engi- 
neering design" of new sublanguages must await a 
more exact theoretical and practical understanding of 
how language function relates to language form. 
5.7 Recommendations 
At present, only a small number of sublanguages 
haw~ been studied in detail. Thus one urgent need is 
to broaden the basis of our understanding of these 
linguistic subsystems. The members of the panel feel 
tlhat this can best be achieved by selecting a few prom- 
i,dng application areas in which to concentrate sub- 
stantive research resources. Such concentration is 
necessary for several reasons. First, most naturally 
occurring sublanguages present real challenges for 
linguistic description. Many months or years of effort 
must usually be invested in describing a corpus of 
texts and in finding the natural extensions of that cor- 
pus in collaboration with speakers of the sublanguage. 
Second, the linguistic peculiarities of the sublanguage 
often present new problems for computational treat- 
ment, particularly if the solutions are to be generaliza- 
ble to other, related sublanguages. Third, many fur- 
ther months of on-site testing are usually necessary to 
properly absorb and evaluate the feedback from users 
of prototype systems, and to evolve more adequate 
versions. The evolution of any significant new system 
therefore implies a substantial collaborative effort over 
a period ranging from several months to several years. 
l\[n parallel with a program of applied research along 
the lines suggested above, we recommend that certain 
kinds of basic research be supported which can both 
feed and be nourished by the applied research. Basic 
research in the areas identified under Sections 5.5 and 
5.6 above should be encouraged in such a way that 
researchers, however theoretically oriented, are 
brought periodically into contact with the practical 
aspects of the proposed real-world applications. Such 
an interplay between the practitioners of basic and 
applied research has proved to be an essential ingredi- 
ent of past advances in sublanguage processing. 
82 American Journal of Computational Linguistics, Volume 8, Number 2. April-June 1982 
Carroll Johnson and Joan Bachenko Proceedings of the Workshop 
6. Acknowledgements 
This workshop is the first in a series organized by 
the Navy Center for Applied Research in Artificial 
Intelligence. The concept for this workshop emerged 
from numerous discussions with Marvin Denicoff and 
Joel Trirnble of ONR, Paul Chapin and Henry Ham- 
burger of NSF, Robert Engelmore and Robert Kahn of 
DARPA, Stanley Wilson and John Davis of NRL, and 
William Price of AFOSR. 
The workshop itself was made possible only 
through the superb cooperation of the ACL. Norm 
Sondheimer, former ACL president, and Don Walker, 
ACL Secretary-Treasurer, used their organizational 
talents to incorporate the workshop into the 1981 ACL 
Conference. Jerry Kaplan, local chairman for the ACL 
meeting, graciously accepted the added responsibility 
of providing local arrangements for the workshop. 
We gratefully acknowledge the very competent 
secretarial assistance by Janet L. Stroup of NRL and 
the careful compilation of the workshop proceedings 
by Veronica Bates of NRL. Financial support for the 
workshop was provided by the Office of Naval Re- 
search. 

References 
Artificial Intelligence Corporation 1981 Intellect Query System 
User's Guide. Release 101. Artificial Intelligence Corp., Walt- 
ham, MA. 
Bicrmann, A. and Ballard, B. 1980 Toward Natural Language 
Computation, AJCL 6 No. 2, 71-86. 
Bobrow, R. 1978 The RUS System. BBN report 3878. Bolt, 
Beranek, and Newman, Inc., Cambridge, MA. 
Bobrow, D., Kaplan, R., Kay, M., Norman, D., Thompson, H., and 
Winograd, T. 1977 GUS, A Frame-Driven Dialogue System. 
Artificial Intelligence 8 155-173. 
Brown, J., Burton, R., and Bell, S. 1974 SOPHIE: A Sophisticated 
Instructional Environment for Teaching Electronic Troubleshooting. 
BBN Report 2790 (March). 
Burton, R. 1976 Semantic Grammar: An Engineering Technique for 
Constructing Natural Language Understanding Systems. BBN 
Report 3453, Bolt, Beranek, and Newman, Inc. Cambridge, MA 
(December). 
Carbonell, J. and Hayes, P. 1981 Dynamic Strategy Section in 
Flexible Parsing. Nineteenth Annual Meeting of the Association 
for Computational Linguistics. Stanford, CA (June). 
Chevalier, L., Dansereau, J., and Poulin, G. 1978 TAUM-METEO: 
Description du Syst~me. Universite de Montreal, Canada. 
Codd, E. 1974 Seven Steps to Rendezvous with the Casual User. 
In Klimbie, J. and Koffeman, K., Eds., Data Base Management. 
North-Holland, Amsterdam: 179-200. 
Codd, E. 1978 How About Recently? (English Dialogue with 
Relational Databases Using RENDEZVOUS Version 1). In 
Shneiderman, B., Ed., Databases: Improving Usability and 
Responsiveness. Academic Press, New York: 3-28. 
Davis, R. 1977 Interactive Transfer of Expertise: Acquisition of 
New Inference Rules. In Proceedings of the Fifth International 
Conference on Artificial Intelligence. Cambridge, MA: 321-328. 
Epstein, M. and Walker, D. 1978 Natural Language Access to a 
Melanoma Data Base. In Proceedings of the Second Annual 
Symposium on Computer Application in Medical Care. IEEE, New 
York: 320-325. 
Grishman, R, and Hirschman, L. 1978 Question Answering from 
Natural Language Data Bases. Artificial Intelligence 25-43. 
Grosz, B. 1981 Focusing and Description in Natural Language 
Dialogues. In Joshi, A., Sag, I., and Webber, B., Eds., Elements 
of Discourse Understanding: Proceedings of a Workshop on Compu- 
tational Aspects of Linguistic Structure and Discourse Setting. 
Cambridge University Press, Cambridge: 84-105. 
Grosz, B. 1977 The Representation and Use of Focus in a System 
for Understanding Dialogs. Proceedings of the Fifth International 
Joint Conference on Artificial Intelligence. Cambridge, MA 
(August 22-25): 67-76. 
Haas, N. and Hendrix, G. 1980 An Approach to Acquiring and 
Applying Knowledge. In Proceedings of the First Annual Nation- 
al Conference on Artificial Intelligence. Stanford University: 
235-239. 
Hendrix, G. and Lewis, W. 1981 Transportable Natural-Language 
Interfaces to Databases. In Proceedings of the Nineteenth Annu- 
al Meeting of the Association for Computational Linguistics. Stan- 
ford, CA (June). 
Hendrix, G., Sacerdoti, E., Sagalowicz, D. and Slocum, J. 1978 
Developing a Natural Language Interface to Complex Data. 
ACM Transactions on Database Systems 3 No. 2 (June) 105-147. 
Harris, Z. 1963 Discourse Analysis Reprints. The Hague, Mouton. 
Hayes, P. and Mouradian, G. 1980 Flexible Parsing. In Proceed- 
ings of of the lSth Annual Meeting of the Association for Compu- 
tational Linguistics. University of Pennsylvania: 97-103. 
Hayes, P. and Reddy, R. 1979 Graceful Interaction in Man- 
Machine Communication. Sixth International Joint Conference 
on Artificial Intelligence. Stanford University, 372-374. 
Hirschman, L., Grishman, R., and Sager, N. 1975 Grammatically- 
Based Automatic Word Class Formation. Information Process- 
ing and Management 11. 
Hirschman, L. and Sager, N. 1981 Automatic Informatting of a 
Medical Sublanguage. In Kittredge and Lehrberger. 
Joshi, A., Mays, E., Lanka, S., and Webber, B, 1981 Natural 
Language Interaction with Dynamic Knowledge Bases: Monitors 
as Responses. In Proceedings of the IJCAI 1981. Vancouver, 
Vancouver (August). 
Kameny, I. et al. 1978 An End User Friendly Interface for Data- 
bases. Proceedings VLDB. Berlin. 
Kaplan, S.J. 1979 Cooperative Responses from a Portable Language 
Data Base Query System. Ph.D. Dissertation, University of Penn- 
sylvania. (Available as Stanford Heuristic Programming Project 
Report HPP-79- 19, Computer Science Department, Stanford 
University, Stanford, CA, 94305 (July).) 
Kaplan, S.J. 1978 Indirect Responses to Loaded Questions. Theo- 
retical Issues in Natural Language Processing-2. University of 
Illinois at Urbana-Champaign (July). 
Kaplan, S.J. and Davidson, J. 1981 Interpreting Natural Language 
Database Updates. In Proceedings of the Nineteenth Annual 
Meeting of the Association for Computational Linguistics. Stan- 
ford, CA, June. 
Kaner, R. and Montgomery, C. 1972 On-Line Bugging: Hope for 
Terminal Cases of Semantic Deviance. Invited Paper at the 
Gordon Research Conference on the Frontiers of Science. New 
London, NH, July. 
Kay, M. 1979 Functional Grammar. In Proceedings of the Fifth 
Annual Meeting of the Berkeley Linguistics Society. 
Kittredge, R. 1981 Variation and Homogeneity of Sublanguages. 
In Kittredge and Lehrberger. 
Kittredge, R. and Lehrberger, J. Eds. 1981 Sublanguage: Studies 
of Language in Restricted Semantic Domains. deGruyter, Berlin. 
Kwasny, S. and Sondheimer, N. 1981 Ungrammaticality and 
Extragrammaticality in Natural Language Understanding Sys- 
tems. In Proceedings of the Seventeenth Annual Meeting of the 
ACL. La Julia, CA (August). 
Carroll Johnson and Joan Bachenko Proceedings of the Workshop 
Landsbergen, S. and Scha, R. 1978 Formal Languages for Seman- 
tic Representation. In Petofi, J., Ed., Aspects of Automated Text 
Processing. Buske, Hamburg. 
Lehrberger, J. 1981 Automatic Translation and the Concept of 
Sublanguage. In Kittredge and Lehrberger. 
Mckeown, K. 1980 Generating Relevant Explanations: Natural 
Language Responses to Questions About Database Structure. 
In Proceedings of First Meeting of AAAI. Stanford, CA 
(August). 
Malhotra, A. 1975 Design Criteria for a Knowledge-Based English 
Language System for Management: An Experimental Analysis. 
MAC TR 146. Cambridge, MA: Project MAC. Massachusetts 
Institute of Technology, February. 
Mark, W. 1980 Rule-Based Inference in Large Knowledge Bases. 
In Proceedings of the First Annual National Conference on Artifi- 
cial Intelligence. Stanford, CA. 
Mays, E. 1980 Failures in Natural Language Systems: Applica- 
tions to Database Query Systems. In Proceedings of the First 
Meeting of AAAI. Stanford, CA, August. 
Miller, L., Heidorn, G., and Jensen, K. 1981 Text-critiquing with 
the EPISTLE System: An Author's Aid to Better Syntax. In 
AFIPS Conference Proceedings. AFIPS Press, Montvale, NJ: 
649-655. 
Moore, R.C. 1981 Problems in Logical Form. In Proceedings of 
the Nineteenth Annual Meeting of the ACL (June). 
Newell, A. and Simon, H. 1972 Human Problem Solving. Prentice- 
Hall, Englewood Cliffs, NJ. 
Novak, G.S., Jr. 1981 Physics Problem Solving: ISAAC-II. In 
Proceedings of the Seventh International Joint Conference on Arti- 
ficial Intelligence. IJCAI-81, Vol. 2. University of British Co- 
lumbia, Vancouver, B.C. (August). 
Petrick, S. 1975 Design of the Underlying Structure for a Data 
Base Retrieval System. In Grishman, R., Ed., Directions in 
Artificial Intelligence: Natural Language Processing. Courant 
Computer Science Report 7, Courant Institute of Mathematical 
Sciences, York University, New York, New NY, 60-93. 
Robinson, A., Appelt, D., Grosz, G., Hendrix, G., and Robinson, J. 
1980 Interpreting Natural Language Utterances in Dialog 
about Tasks. Communications of the ACM in press. SRI Tech- 
nical Note 210. Artificial Intelligence Center, SRI Internation- 
al, Menlo Park, CA. 
Robinson, J. 1982 DIAGRAM: A Grammar for Dialogues. Com- 
munications of the A CM. 
Rychener, M. 1980 Approaches to Knowledge Acquisition: The 
lnstructable Production System Project. In Proceedings of the 
First Annual Conference on Artificial Intelligence. Stanford, CA: 
228-230. 
Sagalowicz, D., Ed. 1980 Mechanical Intelligence: Research and 
Applications. Final Technical Report. Artificial Intelligence 
Center, SRI International. Menlo Park, CA. 
',~agcr, N. 1981 Natural Language Information Processing: A Com- 
puter Grammar of English and Its Applications. Addison-Wesley, 
Reading, MA. 
'Sager, N. 1978 Natural Language Information Formatting: The 
Automatic Conversion of Texts to a Structure Data Base. In 
Yovits, M. and Rubinoff, M., Eds., Advances in Computers. 
Academic Press, New York: 89-162. 
Sager, N. and Grishman, R. 1975 The Restriction Language for 
Computer Grammars. Communications of the ACM 18 390-400. 
Schank, R., Lebowitz, M. and Birnbaum, L. 1980 An Integrated 
Understander. American Journal of Computational Linguistics 6 
13-30. 
Silva, G.M.T., Dwiggins, D.L., Busby, S.G., and Kuhns, J.L. 1979 
A Knowledge-Based Automated Message Understanding Methodolo- 
gy for an Advanced Indications System. OSI Report R79-006, 
Operating Systems, Inc. (February). 
Simmons, R. F. and Chester, D. Relating Sentences and Semantic 
Networks with Clausal Logic. Communications of the ACM, to 
appear. 
Sondheimer, N. and Weischedel, R. 1980 A Rule-Based Approach 
to 111- Formed Input. In Proceedings of the Eighth International 
Conference on Computational Linguistics: 46-53. 
Thompson, F. and Thompson, B. 1975 Practical Natural Language 
Processing: The REL System as Prototype. In Rubinoff, M. 
and Yovits, M.C., Eds., Advances in Computers, Volume 13. 
Academic Press, New York. 
Walker, D. and Hobbs, J. 1981 Natural Language Access to 
Medical Text. In Proceedings of the Fifth Annual Symposium on 
Computer Applications in Medical Care. IEEE, New York. 
Waltz, D.L. 1978 An English Language Question Answering 
System for a Large Relational Data Base. Communications of 
the ACM 21 526-539. 
Weischedel, R. and Black, J. 1979 Responding to Potentially Un- 
parsable Sentences. Tech Rep. 79/3. Department of Computer 
and Information Sciences, Universiy of Delaware, Newark, DE. 
Weischedel, R. and Sondheimer, N. 1981 A Framework for Proc- 
essing Ill-Formed Input. Technical Report. Department of Com- 
puter and Information Sciences, University of Delaware, New- 
ark, DE. 
Wilensky, R. 1978 Understanding Goal-Based Stories. Yale Uni- 
versity Research Report No. 140. 
