DESIGN OF A KNOWLEDGE-BASED REPORT 
GENERATOR 
Karen Kukich 
University of Pittsburgh 
Bell Telephone Laboratories 
Murray ~tll, NJ 07974 
ABSTRACT 
Knowledge-Based Report Generation is a technique 
for automatically generating natural language reports 
from computer databases. It is so named because it 
applies knowledge-based expert systems software to the 
problem of text generation. The first application of the 
technique, a system for generating natural language 
stock reports from a daily stock quotes database, is par- 
tially implemented. Three fundamental principles of the 
technique are its use of domain-specific semantic and 
linguistic knowledge, its use of macro-level semantic and 
linguistic constructs (such as whole messages, a phrasal 
lexicon, and a sentence-combining grammar), and its 
production system approach to knowledge representa- 
tion. 
I. WHAT IS KNOWLEDGE-BASED 
REPORT GENERATION 
A knowledge-based report generator is a computer 
program whose function is to generate natural language 
summaries from computer databases. For example, 
knowledge-based report generators can be designed to 
generate daily stock market reports from a stock quotes 
database, daily weather reports from a meteorological 
database, weekly sales reports from corporate databases, 
or quarterly economic reports from U. S. Commerce 
Department databases, etc. A separate generator must 
be implemented for each domain of discourse because 
each knowledge-based report generator contains 
domain-specific knowledge which is used to infer 
interesting messages from the database and to express 
those messages in the sublanguage of the domain of 
discourse. The technique of knowledge-based report 
generation is generalizable across domains, however, and 
the actual text generation component of the report gen- 
erator, which comprises roughly one-quarter of the code, 
is directly transportable and readily tailorable. 
Knowledge-based report generation is a practical 
approach to text generation. It's three fundamental 
tenets are the following. First, it assumes that much 
domain-specific semantic, linguistic, and rhetoric 
knowledge is required in order for a computer to 
automatically produce intelligent and fluent text. 
Second, it assumes that production system languages, 
such as those used to build expert systems, are well- 
suited to the task of representing and integrating seman- 
tic, linguistic, and rhetoric knowledge. Finally, it holds 
that macro-level knowledge units, such as whole seman- 
tic messages, a phrasal lexicon, clausal grammatical 
categories, and a clause-combining grammar, provide an 
appropriate level of knowledge representation for gen- 
erating that type of text which may be categorized as 
periodic summary reports. These three tenets guide the 
design and implementation of a knowledge-based report 
generation system. 
II. SAMPLE OUTPUT FROM A 
KNOWLEDGE-BASED REPORT GENERATOR 
The first application of the technique of 
knowledge-based report generation is a partially imple- 
mented stock report generator called Aria. Data from a 
Dow Jones stock quotes database serves as input to the 
system, and the opening paragraphs of a stock market 
summary are produced as output. As more semantic and 
linguistic knowledge about the stock market is added to 
the system, it will be able to generate longer, more 
informative reports. 
Figure 1 depicts a portion of the actual data submit- 
ted to Ana for January 12, 1983. A hand drawn graph 
of the same data is included. The following text samples 
are Ana's interpretation of the data on two different 
runs. 
DOW JONES INDUSTRIALS AVERAGE -- 01/12183 
01/12 CLOSE 30 INDUS 1083.61 
01/12 330PM 30 INDUS 1089.40 
01/12 3PM 30 INDUS 1093.44 
01/12 230PM 30 INDUS 1100.07 
01/12 2PM 30 INDUS 1095.38 
01/12 130PM 30 INDUS 1095.75 
01/12 IPM 30 INDUS 1095.84 
01/12 1230PM 30 INDUS 1095.75 
01/12 NOON 30 INDUS 1092.35 
01/12 II30AM 30 INDUS I089.40 
01/12 IIAM 30 INDUS 1085.08 
01/12 1030AM 30 INDUS 1085.36 
01/11 CLOSE 30 INDUS 1083.79 
CLOSING AVERAGE 1083.61 DOWN 0.18 
145 
1102 
1098 
1094 1o9o ~/ ~, 
1086,...--------,t/ -~, 
1082 
10 10:30 11 11:30 12 12:30 1 1:30 2 2:30 3 3:30 4 
Figure 1 
(1) 
after climbing steadily through most of 
the morning , the stock market was pushed 
downhill late in the day stock prices posted 
a small loss , with the indexes turning in a 
mixed showing yesterday in brisk trading . 
the Dow Jones average of 30 industrials 
surrendered a 16.28 gain at 4pro and de- 
clined slightly , finishing the day at 1083.61 
,off 0.18 points. 
(2) 
wall street's securities markets rose 
steadily through most of the morning , before 
sliding downhill late in the day the stock 
market posted a small loss yesterday , with 
the indexes finishing with mixed results in ac- 
tive trading . 
the Dow Jones average of 30 industrials 
surrendered a 16.28 gain at 4pro and de- 
clined slightly , to finish at 1083.61 , off 
0.18 points . 
III. SYSTEM OVERVIEW 
In order to generate accurate and fluent summaries, 
a knowledge-based report generator performs two main 
tasks: first, it infers semantic messages from the data in 
the database; second, it maps those messages into 
phrases in its phrasal lexicon, stitching them together 
according to the rules of its clause-combining grammar, 
and incorporating rhetoric constraints in the process. As 
the work of McKeown I and Mann and Moore 2 demon- 
strates, neither the problem of deciding what to say nor 
the problem of determining how to say it is trivial, and 
as'Appelt 3 has pointed out, the distinction between them 
is not always clear. 
A. System Architecture 
A knowledge-based report generator consists of the 
following four independent, sequential components: 1) a 
fact generator, 2) a message generator, 3) a discourse 
organizer, and 4) a text generator. Data from the data- 
base serves as input to the first module, which produces 
a stream of facts as output; facts serve as input to the 
second module, which produces a set of messages as out- 
put; messages form the input to the third module, which 
organizes them and produces a set of ordered messages 
as output; ordered messages form the input to the fourth 
module, which produces final text as output. The 
modules function independently and sequentially for the 
sake of computational manageability at the expense of 
psychological validity. 
With the exception of the first module, which is a 
straightforward C program, the entire system is coded in 
the OPS5 production system language. 4 At the time that 
the sample output above was generated, module 2, the 
message generator, consisted of 120 production rules; 
module 3, the discourse organizer contained 16 produc- 
tion rules; and module 4, the text generator, included 
109 production rules and a phrasal dictionary of 519 
entries. Real time processing requirements for each 
module on a lightly loaded VAX 11/780 processor were 
the following: phase 1 16 seconds, phase 2 - 34 
seconds, phase 3 - 24 seconds, phase 4 - 1 minute, 59 
seconds. 
B. Knowledge Constructs 
The fundamental knowledge constructs of the sys- 
tem are of two types: 1) static knowledge structures, or 
memory elements, which can be thought of as n- 
dimensional propositions, and 2) dynamic knowledge 
structures, or production rules, which perform pattern- 
recognition operations on n-dimensional propositions, 
Static knowledge structures come in five flavors: facts. 
messages, lexicon entries, medial text elements, and 
various control elements. Dynamic knowledge constructs 
occur in ten varieties: inference productions, ordering 
productions, discourse mechanics productions, phrase 
selection productions, syntax selection productions, ana- 
phora selection productions, verb morphology produc- 
tions, punctuation selection productions, writing produc- 
tions, and various control productions. 
C. Functions 
The function of the first module is to perform the 
arithmetic computation required to produce facts that 
contain the relevant information needed to infer interest- 
ing messages, and to write those facts in the OPS5 
memory element format. For example, the fact that 
indicates the closing status of the Dow Jones Average of 
30 Industrials for January 12, 1983 is: 
(make fact "fname CLb-~rAT "iname DJI "itype 
COMPOS "date 01/12 "hour CLOSE "open- 
level 1084.25 "high-level 1105.13 "low-level 
1075.88 "close-level 1083.61 "cumul-dir DN 
"cumul-deg 0.18) 
The function of the second module is to inter 
interesting messages from the facts using inferencing 
productions such as the following: 
146 
(p instan-mixedup 
(goal "stat act "op instanmixed) 
(fact "(name CLSTAT "iname DJI 
"cumul-dir UP "repdate <date>) 
(fact "(name ADVDEC "iname NYSE 
"advances <x> "declines {<y> > <x>}) 
(make message "top GENMKT "subtop MIX 
"mix mixed "repdate <date> 
"subjclass MKT "tim close) 
(make goal "star pend "op writemessage) 
(remove 1) 
) 
This production infers that if the closing status of the 
Dow had a direction of "up', and yet the number of 
declines exceeded the number of advances for the day, 
then it can be said that the market was mixed. The mes- 
sage that is produced looks like this: 
(make message "repdate 01/12 "top GENMKT 
"subsubtop nil "subtop MIX "subjclass MKT 
"dir nil "deg nil "vardeg I nil \] "varlev I nil \[ "mix 
mixed "chg nil "sco nil "tim close "vartim I nil i 
"dur nil "vol nil "who nil ) 
The inferencing process in phase 2 is hierarchically con- 
trolled. 
Module 3 performs the uncomplicated task of 
grouping messages into paragraphs, ordering messages 
within paragraphs, and assigning a priority number to 
each message. Priorities are assigned as a function of 
topic and subtopic. The system "knows" a default order- 
ing sequence, and it "knows" some exception rules which 
assign higher priorities to messages of special signifi- 
cance, such as indicators hitting record highs. As in 
module 2, processing is hierarchically controlled. Even- 
tually, modules 2 and 3 should be combined so that their 
knowledge could be shared. 
The most complicated processing is performed by 
module 4. This processing is not hierarchically con- 
trolled, but instead more closely resembles control in an 
ATN. Module 4, the text generator, coordinates and 
executes the following activities: 1) selection of phrases 
from the phrasal lexicon that both capture the semantic 
meaning of the message and satisfy rhetorical con- 
straints; 2) selection of appropriate syntactic forms for 
predicate phrases, such as sentence, participial clause, 
prepositional phrase, etc.; 3) selection of appropriate 
anaphora for subject phrases 4) morphological processing 
of verbs; 5) interjection of appropriate punctuation; and 
6) control of discourse mechanics, such as inclusion of 
more than one clause per sentence and more than one 
sentence per paragraph. 
The module 4 processor is able to coordinate and 
execute these activities because it incorporates and 
integrates the semantic, syntactic, and rhetoric 
knowledge it needs into its static and dynamic knowledge 
structures. For example, a phrasal lexicon entry that 
might match the "mixed market" message is the follow- 
ing: 
(make phraselex "top GENMKT "subtop MIX 
"mix mixed "chg nil "tim close "subjtype 
NAME "subjclass MKT *predfs turned Apredfpl 
turned "predpart turning "predinf ~to turnl 
^predrem ~n a mixed showing\] "fen 9 "rand 5 
"imp 11) 
An example of a syntax selection production th,tt would 
select the syntactic form subordinate-participial-clause as 
an appropriate form for a phrase (a~) in "after rising 
steadily through most of the morning") is the following: 
(p 5 .selectsu borpartpre-selectsyntax 
(goal ^stat act "op selectsyntax) ; 1 
(sentreq "sentstat nil) ; 2 
(message "foc in "top <t> "tim <> nil 
"subjclass <sc>) ; 3 
(message "foc nil "top <t> "tim <> nil 
"subjclass <sc>) ; 4 
(paramsynforms "suborpartpre <set>) 
(randnum "randval < <set>) 
(lastsynform "form << initsent prepp >> ) 
- (openingsynform "form 
< < suborsent suborpart > >) 
- (message "foc in "tim close) 
-.> 
(remove 1) 
(make synform "form suborpart ) 
(modify 4 "foc peek ) 
(make goal "star act "op selectsubor) 
D. Context-Dependent Grammar 
Syntax selection productions, such as the examt)le 
above, comprise a context-dependent, right-branching, 
clause-combining grammar. Because of the attribute- 
value, pattern-recognition nature of these grammar rules 
and their use of the lexicon, they may be viewed as a 
high-level variant of a lexical functional grammar. 5 The 
efficacy of a low-level functional grammar for text gen- 
eration has been demonstrated in McKeown's TEXT sys- 
tem. 6 
For each message, in sequence, the system first 
selects a predicate phrase that matches the semantic con- 
tent of the message, and next selects a syntactic form. 
such as sentence or prepositional phrase, into which form 
the predicate phrase may be hammered. The system's 
default goal is to form complex sentences by combining a 
variable number of messages expressed m a variety of 
syntactic forms in each sentence. Every message may be 
expressed in the syntactic form of a simple sentence. 
But under certain grammatical and rhetorical conditions, 
which are specified in the syntax selection productions, 
and which sometimes include looking ahead at the next 
sequential message, the system opts for a different syn- 
tactic form. 
The right-branching behavior of the system implies 
that at any point the system has the option to lay down a 
period and start a new ~ntence. It also implies that 
embedded subject-complement forms, such as relative 
;5 
;6 
;7 
147 
clauses modifying subjects, are trickier to implement 
(and have not been implemented as yet). That embed- 
ded subject complements pose special difficulties should 
not be considered discouraging. Developmental linguis- 
tics research reveals that "operations on sentence sub- 
jects, including subject complementation and relative 
clauses modifying subjects" are among the last to appear 
in the acquisition of complex sentences, 7 and a 
knowledge-based report generator incorporates the basic 
mechanism for eventually matching messages to nominal- 
izations of predicate phrases to create subject comple- 
ments, as well as the mechanism for embedding relative 
clauses. 
IV. THE DOMAIN-SPECIFIC 
KNOWLEDGE REQUIREMENT TENET 
How does one determine what knowledge must 
incorporated into a knowledge-based report generator? 
Because the goal of a knowledge-based report generator 
is to produce reports that are indistinguishable from 
reports written by people for the same database, it is log- 
ical to turn to samples of naturally generated text from 
the specific domain of discourse in order to gain insights 
into the semantic, linguistic, and rhetoric knowledge 
requirements of the report generator. 
Research in machine translation s and text under- 
standing 9 has demonstrated that not only does naturally 
generated text disclose the lexicon and grammar of a 
sublanguage, but it also reveals the essential semantic 
classes and attributes of a domain of discourse, as well 
as the relations between those classes and attributes. 
Thus, samples of actual text may be used to build the 
phrasal dictionary for a report generator and to define 
the syntactic categories that a generator must have 
knowledge of. Similarly, the semantic classes, attributes 
and relations revealed in the text define the scope and 
variety of the semantic knowledge the system must 
incorporate in order to infer relevant and interesting 
messages from the database. 
Ana's phrasal lexicon consists of subjects, such as 
"wall street's securities markets", and predicates, such as 
"were swept into a broad and steep decline", which are 
extracted from the text of naturally generated stock 
reports, The syntactic categories Ann knows about are 
the clausal level categories that are found in the same 
text, such as, sentence, coordinate-sentence, 
subordinate-sentence, subordinate-participial-clause, 
prepositional-phrase, and others. 
Semantic analysis of a sample of natural text stock 
reports discloses that a hierarchy of approximately forty 
message classes accounts for nearly all of the semantic 
information contained in the "core market sentences" of 
stock reports. The term "core market sentences" was 
introduced by Kittredge to refer to those sentences which 
can be inferred from the data in the data base without 
reference to external events such as wars, strikes, and 
corporate or government policy making. 1° Thus, for 
example, Ana could say "Eastman Kodak advanced 2 3/4 
to 85 3/4;" but it could not append "it announced 
development of the world's fastest color film for delivery 
in 1983.". Aria currently has knowledge of only six mes- 
sage classes. These include the closing market status 
message, the volume of trading message, and the mixed 
market message, the interesting market fluctuations mes- 
sage, the closing Dow status message, and the interesting 
Dow fluctuations message. 
V. THE PRODUCTION SYSTEM 
KNOWLEDGE REPRESENTATION TENET 
The use of production systems for natural language 
processing was suggested as early as 1972 by Heidorn,ll 
whose production language NLP is currently being used 
for syntactic processing research. A production system 
for language understanding has been implemented in 
OPS5 by Frederking. 12 Many benefits are derived from 
using a production system to represent the knowledge 
required for text generation. Two of the more important 
advantages are the ability to integrate semantic, syntac- 
tic, and rhetoric knowledge, and the ability to extend 
and tailor the system easily. 
A. Knowledge Integration 
Knowledge integration is evident in the production 
rule displayed earlier for selecting the syntactic form of 
subordinate participial clause. In English, that produc- 
tion said: 
IF 
1) there is an active goal to select a syntactic form 
2) the sentence requirement has not been satisfied 
3) the message currently in focus has topic <t>, 
subject class <sc>, and some non-nil time 
4) the next sequential message has the same topic. 
subject class, and some non-nil time 
5) the subordinate-participial-clause parameter 
is set at value <set> 
6) the current random number is less than <set> 
7) the last syntactic form used was either a 
prepositional phrase or a sentence initializer 
8) the opening syntactic form of the last sentence 
was not a subordinate sentence or a 
subordinate participial clause 
9) the time attribute of the message in focus 
does not have value 'close' 
THEN 
1) remove the goal of selecting a syntactic form 
2) make the current syntactic form a subordinate 
participial clause 
3) modify the next sequential message to put it 
in peripheral focus 
4) set a goal to select a subordinating conjunction. 
It should be apparent from the explanation that the rule 
integrates semantic knowledge, such as message topic 
and time, syntactic knowledge, such as whether the 
sentence requirement has been satisfied, and rhetoric 
knowledge, such as the preference to avoid using subor- 
dinate clauses as the opening form of two consecutive 
sentences. 
148 
B. Knowledge Tailoring and Extending 
Conditions number 5 and 6, the syntactic form 
parameter and the random number, are examples of con- 
trol elements that are used for syntactic tailoring. A 
syntactic form parameter may be preset at any value 
between 1 and 11 by the system user. A value of 8, for 
example, would result in an 80 percent chance that the 
rule in which the parameter occurs would be satisfied if 
all its other conditions were satisfied. Consequently, on 
20 percent of the occasions when the rule would have 
been otherwise satisfied, the syntactic form parameter 
would prevent the rule from firing, and the system 
would be forced to opt for a choice of some other syn- 
tactic form. Thus, if the user prefers reports that are low 
on subordinate participial clauses, the subordinate parti- 
cipial clause parameter might be set at 3 or lower. 
The following production contains the bank of 
parameters as they were set to generate text sample (2) 
above: 
(p _ l.setparams 
(goal "stat act "op setparams) 
(remove 1) 
(make paramsyllables "val 30) 
(make parammessages "val 3) 
(make paramsynforms 
"sentence 11 
"coorsent 11 
"suborsent 11 
"prepphrase 11 
"suborsentpre 5 
"suborpartpre 8 
"suborsentpost 8 
"suborpartpost 11 
"subol'partsentpost I 1 
When sample text (1) was generated, all syntactic form 
parameters were set at 11. The first two parameters in 
the bank are rhetoric parameters. They control the 
maximum length of sentences in syllables (roughly) and 
in number of messages per sentence. 
Not only does production system knowledge 
representation allow syntactic tailoring, but it also per- 
mits semantic tailoring. Aria could be tailored to focus 
on particular stocks or groups of stocks to meet the 
information needs of individual users. Furthermore, a 
production system is readily extensible. Currently, Ana 
has only a small amount of general knowledge about the 
stock market and is far from a stock market expert. But 
any knowledge that can be made explicit can be added to 
the system prolonged incremental growth in the 
knowledge of the system could someday result in a sys- 
tem that truly is a stock market expert. 
Vl. THE MACRO-LEVEL 
KNOWLEDGE CONSTRUCTS TENET 
The problem of dealing with the complexity of 
natural language is made much more tractable by work- 
ing in macro-level knowledge constructs, such as seman- 
tic units consisting of whole messages, lexical iter-¢ ~,~,a- 
sisting of whole phrases, syntactic categories at the 
clause level, and a clause-combining grammar. Macro- 
level processing buys linguistic fluency at the cost of 
semantic and linguistic flexibility. However, the loss of 
flexibility appears to be not much greater than the con- 
straints imposed by the grammar and semantics of the 
sublanguage of the domain of discourse. Furthermore, 
there may be more to the notion of macro-level semantic 
and linguistic processing than mere computational 
manageability. 
The notion of a phrasal lexicon was suggested by 
Becker, 13 who proposed that people generate utterances 
"mostly by stitching together swatches of text that they 
have heard before. Wilensky and Arens have experi- 
mented with a phrasal lexicon in a language understand- 
ing system. 14 I believe that natural language behavior 
will eventually be understood in terms of a theory of 
stratified natural language processing in which macro- 
level knowledge constructs, such as those used in a 
knowledge-based report generator, occur at one of the 
higher cognitive gtrata. 
A poor but useful analogy to mechanical gear- 
shifting while driving a car can be drawn. Just as driv- 
ing in third gear makes most efficient use of an 
automobile's resources, so also does generating language 
in third gear make most efficient use of human informa- 
tion processing resources. That is, matching whole 
phrases and applying a clause-combining grammar is 
cognitively economical. But when only a near match for 
a message can be found in a speaker's phrasal diction- 
ary, the speaker must downshift into second gear, and 
either perform some additional processing on the nhrase 
to transform it into the desired form to match the mes- 
sage, or perform some processing on the message to 
transform it into one that matches the phrase. And if 
not even a near match for a message can be found, the 
speaker must downshift into first gear and either con- 
struct a phrase from elementary texicai items, including 
words, prefixes, and suffixes, or reconstruct the mes- 
sage. 
As currently configured, a knowledge-based text 
generator operates only in third gear. Because the units 
of processing are linguistically mature whole phrases, the 
report generation system can produce fluent text without 
having the detailed knowledge-needed to construct 
mature phrases from their elementary components. But 
there is nothing except the time and insight of a system 
implementor to prevent this detailed knowledge from 
being added to the system. By experimenting with addi- 
tional knowledge, a system could gradually be extended 
to shift into lower gears, to exhibit greater interaction 
between semantic and linguistic components, and to do 
more flexible, if not creative, generation of semantic 
149 
messages and linguistic phrases. A knowledge-based 
report generator may be viewed as a starting tool for 
modeling a stratiform theory of natural language pro- 
cessing. 
VII. CONCLUSION 
Knowledge-based report generation is practical 
because it tackles a moderately ill-defined problem with 
an effective technique, namely, a macro-level, 
knowledge-based, production system technique. Stock 
market reports are typical instances of a whole class of 
summary-type periodic reports for which the scope and 
variety of semantic and linguistic complexity is great 
enough to negate a straightforward algorithmic solution, 
but constrained enough to allow a high-level cross-wise 
slice of the variety of knowledge to be effectively incor- 
porated into a production system. Even so, it will be 
some time before the technique is cost effective. The 
time required to add knowledge to a system is greater 
than the time required to add productions to a traditional 
expert system. Most of the time is spent doing seman- 
tic analysis for the purpose of creating useful semantic 
classes and attributes, and identifying the relations 
between them. Coding itself goes quickly, but then the 
system must be tested and calibrated (if the guesses on 
the semantics were close) or redone entirely (if the 
guesses were not close). Still, the initial success of the 
technique suggests its value both as a basic research tool, 
for exploring increasingly more detailed semantic and 
linguistic processes, and as an applied research tool, for 
designing extensible and tailorable automatic report gen- 
erators. 
ACKNOWLEDGEMENT 
l'wish to express my deep appreciation to Michael 
Lesk for his unfailing guidance and support in the 
development of this project. 
REFERENCES 
1. Kathleen R. McKeown, "The TEXT System for 
Natural Language Generation: An Overview," 
Proceedings of the Twentieth Annual Meeting of the 
Association for Computational Linguistics, Toronto, 
Canada (1982). 
2. James A. Moore and William C. Mann, "'A 
Snapshot of KDS: A Knowledge Delivery System," 
in Proceedings of the 17th Annual Meeting of the 
Association for Computational linguistics, La Jolla, 
California (11-12 August 1979). 
3. Douglas E. Appelt, "Problem Solving Applied to 
Language Generation," pp. 59-63 in Proceedings of 
the 18th Annual Meeting of the Association for Com- 
putational Linguistics, University of Pennsylvania, 
Philadelphia, PA (June 19-22,1980). 
4. C.L. Forgy, "OPS-5 User's Manual," CMU-CS- 
81-135, Dept of Computer Science, Carnegie- 
Mellon University, Pittsburgh, PA 15213 (July 
1981). 
5. Joan Bresnan and Ronald M. Kaplan, "Lexical- 
Functional Grammar: A Formal System for Gram- 
matical Representation," Occasional Paper #13, 
MIT Center for Cognitive Science (1982). 
6. Kathleen Rose McKeown, "Generating Natural 
Language Text in Response to Questions about 
Database Structure," Doctoral Dissertation, 
University of Pennsylvania Computer and Informa- 
tion Science Department (1982). 
7. Melissa Bowerman, "The Acquisition of Complex 
Sentences," pp. 285-305 in Language Acquisition, 
ed. Michael Garman, Cambridge University Press, 
Cambridge (1979). 
8. Richard Kittredge and John Lehrberger, Sub- 
languages: Studies of Language in Restricted Seman- 
tic Domains, Walter DeGruyter, New York (in 
press). 
9. Naomi Sager, "Information Structures in Texts of a 
Sublanguage," in The Information Communi~: Alli- 
ance for Progress - Proceedings of the 44th ASIS 
Annual Meeting, Volume 18, Knowlton Industry 
Publications for the American Society for Informa- 
tion Science, White Plains, N.Y. (October 1981). 
IO. Richard I. Kittredge, "Semantic Processing of 
Texts in Restricted Sublanguages," Computers and 
Mathematics with Applications 8(0), Pergamon Press 
(1982). 
11. George E. Heidorn, "Natural Language Inputs to a 
Simulation Programming System,'" NPS- 
55HD72101A, Naval Postgraduate School, Mon- 
terey, CA (October 1972). 
12. Robert E. Frederking, A Production System 
Approach to Language Understanding, To appear 
(1983). 
13. Joseph Becket, "The Phrasal Lexicon," pp. 70-73 
in Theoretical Issues in Natural Language Process- 
ing, ed. B. I. Nash-Webber, Cambridge, Mas- 
sachusetts (10-13 June 1975). 
14. Robert Wilensky and Yigel Arens, "'PHRAN -- A 
Knowledge-Based Natural Language Under- 
stander," pp. 117-121 in Proceedings of the 18th 
Annual Meeting of the Association for Computational 
Linguistics, University of Pennsylvania. Philadel- 
phia, Pennsylvania (June 19-22, 1980). 
1-50 
