TOWARDS A THEORY OF COMPREHENSION OF DECLARATIVE CONTEXTS 
Fernando Gomez 
Department of Computer Science 
University of Central Florida 
Orlando, Florida 32816 
ABSTRACT 
An outline of a theory of comprehension of 
declarative contexts is presented. The main aspect 
of the theory being developed is based on Kant's 
distinction between concepts as rules (we have 
called them conceptual specialists) and concepts 
as an abstract representation (schemata, frames). 
Comprehension is viewed as a process dependent on 
the conceptual specialists (they contain the infe- 
rential knowledge), the schemata or frames (they 
contain the declarative knowledge), and a parser. 
The function of the parser is to produce a segmen- 
tation of the sentences in a case frame structure, 
thus determininig the meaning of prepositions, 
polysemous verbs, noun group etc. The function of 
this parser is not to produce an output to be in- 
terpreted by semantic routines or an interpreter~ 
but to start the parsing process and proceed until 
a concept relevant to the theme of the text is 
recognized. Then the concept takes control of the 
comprehension process overriding the lower level 
linguistic process. Hence comprehension is viewed 
as a process in which high level sources of know- 
ledge (concepts) override lower level linguistic 
processes. 
i. Introduction 
This paper deals with a theory of computer 
comprehension of descriptive contexts. By 
"descriptive contexts" I refer to the language of 
scientific books, text books, this text, etc.. In 
the distinction performative vs. declarative, 
descriptive texts clearly fall in the declarative 
side. Recent work in natural language has dealt 
with contexts in which the computer understanding 
depends on the meaning of the action verbs and the 
human actions (plans, intentions, goals) indicated 
by them (Schank and Abelson 1977; Grosz 1977; 
Wilensky 1978; Bruce and Newman 1978). Also a 
considerable amount of work has been done in a 
plan-based theory of task oriented dialogues (Cohen 
and Perrault 1979; Perrault and Allen 1980; Hobbs 
and Evans 1980). This work has had very little 
bearing on a theory of ~omputer understanding of 
descriptive contexts. One of the main tenets of 
the proposed research is that descriptive (or 
declarative as we prefer to call them) contexts 
call for different theoretical ideas compared 
to those proposed for the understanding of human 
actions, although~ naturally there are aspects that 
are common. 
An important characteristic of these contexts 
is the predominance of descriptive predicates and 
verbs (verbs such as "contain," "refer," "consist 
of," etc.) over action verbs. A direct result of 
this is that the meaning of the sentence does not 
depend as much on the main verb of the sentence as 
on the concepts that make it up. Hence meaning 
representations centered in the main verb of the 
sentence are futile for these contexts. We have 
approached the problem of comprehension in these 
contexts by considering concepts both as active 
agents that recognize themselves and as an abstract 
representation of the properties of an object. This 
aspect of the theory being developed is based on 
Kant's distinction between concepts as rules (we 
have called them conceptual specialists) and con- 
cepts as an abstract representation (frames, sche- 
mata). Comprehension is viewed as a process depen- 
dent.on the conceptual specialists (they contain 
the inferential knowledge), the schemata (they con- 
tain structural knowledge), and a parser. The 
function of the parser is to produce a segmentation 
of the sentences in a case frame structure, thus 
determining the meaning of prepositions, polysemous 
verbs, noun group, etc.. But the function of this 
parser is not to produce an output to be interpre- 
ted by semantic routines, but to start the parsing 
process and to proceed until a concept relevant to 
the theme of the text is recognized. Then the 
concept (a cluster of production rules) takes con- 
trol of the comprehension process overriding the 
lower level linguistic processes. The concept 
continues supervising and guiding the parsing until 
the sentence has been understood, that is, the 
meaning of the sentence has been mapped into the 
final internal representation. Thus a text is 
parsed directly into the final knowledge structures. 
Hence comprehension is viewed as a process in which 
high level sources of knowledge (concepts) override 
lower level linguistic processes. We have used 
these ideas to build a system, called LLULL, to 
unde{stand programming problems taken verbatim from 
introductory books on programming. 
2. Concepts, Schemata and Inferences 
In Kant's Critique of Pure Reason one may find 
two views of a concept. According to one view, a 
concept is a system of rules governing the applica- 
tion of a predicate to an object. The rule that 
36 
tells us whether the predicate "large" applies to 
the concept Canada is a such rule. The system of 
rules that allows us to recognize any given 
instance of the concept Canada constitutes our 
concept of Canada. According to a second view, 
Kant considers a concept as an abstract represen- 
tation (vorstellung) of the properties of an 
object. This second view of a concept is akin to 
the notion of concept used in such knowledge 
representation languages as FRL, KLONE and KIIL. 
Frames have played dual functions. They have 
been used as a way to organize the inferences, and 
also as a structural representation of what is re- 
membered of a given situation. This has caused 
confusion between two different cognitive aspects: 
memory and comprehension (see Ortony, 1978). We 
think that one of the reasons for this confusion 
is due to the failure in distinguishing between 
the two types of concepts (concepts as rules and 
concepts as a structural representation). We have 
based our analysis on Kant's distinction in order 
to separate clearly between the organization of 
the inferences and the memory aspect. For any 
given text, a thematic frame contains structural 
knowledge about what is remembered of a theme. 
One of the slots in this frame contains a list of 
the relevant concepts for that theme. Each of 
these concepts in this list is separately organized 
as a cluster of production rules. They contain 
the inferential knowledge that allows the system 
to interpret the information being presently 
processed, to anticipate incoming information, and 
to guide and supervise the parser (see below). In 
some instances, the conceptual specialists access 
the knowledge stored in the thematic frame to per- 
form some of these actions. 
3. Linguistic Knowledge, Text Understanding 
and P arsin$ 
In text understanding, there are two distinct 
issues. One has to do with the mapping of individ- 
ual sentences into some internal representation 
(syntactic markers, some type of case grammar, 
Wilks' preference semantics, Schank's conceptual 
dependency etc.). In designing this mapping, 
several approaches have been taken. In Winograd 
(1972) and Marcus (1979), there is an interplay 
between syntax, and semantic markers (in that 
order), while in Wilks (1973) and Riesbeck (1975) 
the parser rely almost exclusively on semantic 
categories. 
A separate issue has to do with the meaning 
of the internal representation in relation to the 
understanding of the text. For instance, consider 
the following text (it belongs to the second 
example): 
"A bank would like to produce records 
of the transactions during an account- 
ing period in connection with their 
checking accounts. For each account 
the bank wants a list showing the 
balance at the beginning of t1~e 
period, the number of deposits and 
withdrawals, and the final balance." 
Assume that we parse these sentences into our 
favorite internal representation. Now what we do 
with the internal representation? It is still far 
distant from its textual meaning. In fact, the 
first sentence is only introducing the topic of the 
programming problem. The writer could have 
achieved the same effect by saying: "The following 
is a checking account problem". The textual mean- 
ing of the second sentence is the description of 
the output for that problem. The writer could have 
achieved the same effect by saying that the output 
for the problem consists of the old-balance, 
deposits, withdrawals, etc.. One way to produce 
the textual meaning of the sentence is to interpret 
the internal representation that has already been 
built. Of course, that is equivalent to reparsing 
the sentence. Another way is to map the sentence 
directly into the final representation or the 
textual meaning of the sentence. That is the 
approach we have taken. DeJong (1979) and Schank 
etal. (1979) are two recent works that move in 
that direction. DeJong's system, called FRUMP, is 
a strong form of top down parser. It skims the 
text looking for those concepts in which it is 
interested. When it finds all of them, it ignores 
the remainder of the text. In analogy to key-word 
parsers, we may describe FRUMP as a key-concept 
parser. In Schank etal. (1979), words are marked 
in the dictionary as skippable or as having high 
relevance for a given script. When a relevant word 
is found, some questions are formulated as requests 
to the parser. These requests guide the parser in 
the understanding of the story. In our opinion, 
the criteria by which words are marked as skippable 
or relevant are not clear. 
There are significant differences between our 
ideas and those in the aforementioned works. The 
least signi£icant o~ them is that the internal 
representation selected by us has been a type of 
case grammar, while in those works the sentences 
are mapped into Schank's conceptual dependency 
notation. Due to the declarative nature of the 
texts we have studied, we have not seen a need for 
a deeper representation of the action verbs. The 
most important difference lies in the incorporation 
in our model of Kant's distinction between concepts 
as a system of rules and concepts as an abstract 
representation (an epistemic notion that is absent 
in Schank and his collobarators' work). The in- 
clusion of this distinction in our model makes the 
role and the organization of the different compo- 
nents that form part of comprehension differ 
markedly from those in the aforementioned works. 
4. Organization and Communication between 
the System Components 
The organization that we have proposed appears 
in Fig. I. Central to the organization are the 
conceptual specialists. The other components are 
subordinated to them. 
37 
I ACTIVE FRAMES I 
FJ.$ure 1 Sys=em Orsanizai::Lon 
• "ne parser is essentially based on semantic markers 
and parses a sentence in to a case frame structure. 
The specialists contain contextual knowledge rele- 
vant to each ~pecific topic. This knowledge is 6f 
inferential type. What we have termed "passive 
frames" contain what the system remembers of a 
given topic. At the beginning of the parsing pro- 
cess, the active frames contain nothing. At the 
end of the process, the meaning of the text will 
be recorded in them. Everything in these frames, 
including the name of the slots, are built from 
scratch by the conceptual specialists. 
The communication between these elements is 
as follows. When a text is input to the system, 
the parser begins to parse the first sentence. In 
the parser there are mechanisms to recognize the 
passive frame associated with the text. Once this 
is done, mechanisms are set on to check if the most 
recent parsed conceptual constituent of the sen- 
tence is a relevant concept. This is done slmply 
by checking if the concept belongs to the list of 
relevant concepts in the passive frame. If that is 
the case the specialist (concept) override the 
parser. What does this exactly mean? It does not 
mean that the specialist will help the parser to 
produce the segmentation of the sentence, in a way 
similar to Winograd's and Marcus' approaches in 
which semantic selections help the syntax component 
of the parser to produce the right segmentation of 
the sentence. In fact when the specialists take 
over the segmentation of the sentence stops. That 
is what "overriding lower linguistic processes" 
exactly means. The specialist has knowledge to 
interpret whatever structure the parser has built 
as well as to make sense directly of the remaining 
constituents in the rest of the sentence. "To in- 
terpret" and "make sense directly" means that the 
constituents of the sentence will be mapped direct- 
ly into the active frame that the conceptual 
specialists are building. However this does not 
mean that the parser will be turned off. The par- 
ser continues functioning, not in order to continue 
with the segmentation of the sentence but to return 
the remaining of the conceptual constituents of the 
sentence to the specialist in control when asked by 
it. Thus what we have called "linguistic know- 
ledge" has been separated from the high level 
"inferential knowledge" that is dependent on the 
subject matter of a given topic as well as from 
the knowledge that is recalled from a given 
situation. These three different cognitive aspects 
correspond to what we have called "parser," "con- 
ceptual specialists," and "passive frames" 
respectively. 
5. The Parser 
In this section we explain some of the compo- 
nents of the parser so that the reader can follow 
the discussion of the examples in the next section. 
We refer the reader to Gomez (1981) for a detailed 
description of these concepts. Noun Group: The 
function that parses the noun group is called 
DESCRIPTION. DESCR is a semantic marker used to 
mark all words that may form part of a noun group. 
An essential component of DESCRIPTION is a mecha- 
nism to identify the concept underlying the complex 
nominals (cf. Levi, 1978). See Finin (1980) for 
a recent work on complex nominals that concen- 
trates on concept modification. This is of most 
importance because it is characteristic of declar- 
ative contexts that the same concept may be 
referred to by different complex nominals. For in- 
stance, it is not rare to find the following com- 
plex nominals in the same programming problem all 
of them referring to the same concept: "the 
previous balance," "the starting balance," "the 
old balance" "the balance at the beginning of the 
period." DESCRIPTION will return with the same 
token (old-bal) in all of these cases. The reader 
may have realized that "the balance at the beginn- 
ing of the period" is not a compound noun. They 
are related to compound nouns. In fact many com- 
pound nouns have been formed by deletion of prepo- 
sitions. We have called them prepositional 
phrases completing a description, and we have 
treated them as complex nominals. Prepositions: 
For each preposition (also for each conjunction) 
there is a procedure. The function of these pre- 
positional experts (cf. Small, 1980) is =o deter- 
mine the meaning of the preposition. We refer to 
them as FOR-SP, ON-SP, AS-SP, etc.. Descri~tiue 
Verbs: (D-VERBS) are those used to describe. We 
have categorized them in four classes. There are 
those that describe the constituents of an object. 
Among them are: consist of, show, include, be 
~iven by, contain, etc.. We refer to them as 
CONSIST-OF D-VERBS. A second class are those 
used to indicate that something is representing 
something. Represent, indicate, mean, describe, 
etc.. belong to this class. We refer to them as 
REPRESENT D-VERBS. A third class are those that 
fall under the notion of appear. To this class 
belong appear, belong, be $iven on etc.. We refer 
to them as APPEAR D-VERBS. The fourth class are 
formed by those that express a spatial relation. 
Some of these are: follow, precede , be followed 
by any spatial verb. We refer to them as SPATIAL 
D-VERBS. Action Verbs: We have used different 
semantic features, which indicate different levels 
of abstraction, to tag action verbs. Thus we have 
used the marker SUPL to mark in the dictionary 
"supply", "provide", "furnish", but not "offer". 
From the highest level of abstraction all of them 
are tagged with the marker ATRANS. The procedures 
that parse the action verbs and the descriptive 
verbs are called ACTION-VERB and DESCRIPTIVE-VERB 
respectively. 
6. Recognition of C~ ~pts 
The concepts relevant to a programming topic 
are grouped in a passive frame. We distinguish 
between those concepts which are relevant to a 
38 
specific programming task, like balance to check- 
ing-account programs, and those relevant to any 
kind of program, like output, inRut, end-of-data, 
etc.. The former can be only recognized when the 
programming topic has been identified. A concept 
like output will not only be activated by the word 
"output" or by a noun group containing that word. 
The verb "print" will obviously activate that con- 
cept. Any verb that has the feature REQUEST, a 
semantic feature associated with such verbs as 
"like," "want," "need," etc., will activate also 
the concept output. Similarly nominal concepts 
like card and verbal concepts like record, a se- 
mantic feature for verbs like "record," "punch," 
etc. are Just two examples of concepts that will 
activate the input specialist. 
The recognition of concepts is as follows: 
Each time that a new sentence is going to be read, 
a global variable RECOG is initialized to NIL. 
Once a nominal or verbal concept in the sentence 
has been parsed, the function RECOGNIZE-CONCEPT is 
invoked (if the value of RECOG is NIL). This 
function checks if the concept that has been parsed 
is relevant to the progran~ning task in general or 
(if the topic has been identified) is relevant to 
the topic of the programming example. If so, 
RECOGNIZE-CONCEPT sets RECOG to T and passes con- 
trol to the concept that takes control overriding 
the parser. Once a concept has been recognized, 
the specialist for that concept continues in con- 
trol until the entire sentence has been processed. 
The relevant concept may be the subject or any 
other case of the sentence. However if the rele- 
vant concept is in a prepositional phrase that 
starts a sentence, the relevant concept will not 
take control. 
The following data structures are used during 
parsing. A global variable, STRUCT, holds the re- 
sult of the parsing. STRUCT can be considered as a 
STM (short term memory) for the low level linguis- 
tic processes. A BLACKBOARD (Erman and Lesser, 
1975) is used for communication between the high 
level conceptual specialists and the low level 
linguistic experts. Because the information in the 
blackboard does not go beyond the sentential level, 
it may be considered as STM for the high level 
sources of knowledge. A global variable WORD holds 
the word being examined, and WORDSENSE holds the 
semantic features of that word. 
7. Example 1 
An instructor records the name and five test 
scores on a data card for each student. The regis- 
trar also supplies data cards containing a student 
name, identification number and number of courses 
passed. 
The parser is invoked by activating SENTENCE. 
Because "an" has the marker DESCR, SENTENCE passes 
control to DECLARATIVE which handles sentences 
starting with a nominal phrase. (There are other 
functions that respectively handle sentences start- 
ing with a prepositional phrase, an adverbial 
clause, a co~nand, an -ing form, and sentences 
introduced by "to be" (there be, will be, etc.) 
with the meaning of existence.) DECLARATIVE in- 
vokes DESCRIPTION. This parses "an instructor" ob- 
taining the concept instructor. Before returning 
control, DESCRIPTION activates the functions RECOG- 
NIZE-TOPIC and RECOGNIZE-CONCEPT. The former 
function checks in the dictionary if there is a 
frame associated with the concept parsed by 
DESCRIPTION. The frame EXAM-SCORES is associated 
with instructor, then the variable TOPIC is instan- 
tiated to that frame. The recognition of the frame, 
which may be a very hard problem, is very simple 
in the programming problems we have studied and 
normally the first guess happens to be correct. 
Next, RECOGNIZE-CONCEPT is invoked. Because 
instructor does not belong to the relevant concepts 
of the EXAM-SCORES frame, it returns control. 
Finally DESCRIPTION returns control to DECLARATIVE, 
along with a list containing the semantic features 
of instructor. DECLARATIVE, after checking that 
the feature TIME does not belong to those features, 
inserts SUBJECT before "instructor" in STRUCT. Be- 
fore storing the content of WORD, "records," into 
STRUCT, DECLARATIVE invokes RECOGNIZE-CONCEPT to 
recognize the verbal concept. All verbs with the 
feature record, as we said above, activate the in- 
put specialist, called INPUT-SP. When INPUT-SP 
is activated, STRUCT looks like (SUBJ (INSTUCTOR)). 
As we said in the introduction, the INPUT special- 
ist is a collection of production rules. One of 
those rules says: 
IF the marker RECORD belongs to WORDSENSE 
then activate the function ACTION- 
VERB and pass the following reco- 
mmendations to it: l)activate the 
INPUT-SUPERVISOR each time you find 
an object 2) if a RECIPIENT case is 
found then if it has the feature HVM_AN, 
parse and ignore it. Otherwise awaken 
the INPUT-SUPERVISOR 3) if a WHERE case 
(the object where something is recorded) 
is found, awaken the INPUT-SUPERVISOR. 
The INPUT-SUPERVISOR is a function that is 
controlling the input for each particular problem. 
ACTION-VERB parses the first object and passes it 
to the INPUT-SUPERVISOR. This checks if the seman- 
tic feature IGENERIC (this is a semantic feature 
associated with words that refer to generic infor- 
mation like "data," "information," etc.) does not 
belong to the object that has been parsed by 
ACTION-VERB. If that is not the case, the INPUT- 
SUPERVISOR, after checking in the PASSIVE-FRAME 
that name is normally associated with the input 
for EXAM-SCORES, inserts it in the CONSIST-OF slot 
of input. The INPUT-SUPERVISOR returns control to 
ACTION-VERB that parses the next object and the 
process explained above is repeated. 
When ACTION-VERB finds the preposition "on," 
the routine ON-SP is activated. This, after check- 
ing that the main verb of the sentence has been 
parsed and that it takes a WHERE case, checks the 
BLACKBOARD to find out if there is a recommendation 
for it. Because that is the case, ON-SP tells 
DESCRIPTION to parse the nominal phrase "on data 
cards". This returns with the concept card. ON- 
SP activates the INPUT-SUPERVISOR with card. This 
routine, after checking that cards is a type of 
input that the solver handles, inserts "card" in 
39 
the INPUT-TYPE slot of input and returns control. 
What if the sentence had said "... on a notebook"? 
Because notebook is not a form of input, the INPUT -~ 
SUPERVISOR would have not inserted "book" into the 
INPUT-TYPE slot. Another alternative is to let the 
INPUT-SUPERVISOR insert it in the INPUT-TYPE slot 
and let the problem solver make sense out of it. 
There is an interesting tradeoff between under- 
standing and problem solving in these contexts. 
The robuster the understander Is~ the weaker the 
solver may bed and vice versa. The prepositional 
phrase "for each student" is parsed similarly. 
ACTION-VERB returns control to INPUT-SP that in- 
serts "instructor" in the SOURCE slot of input. 
Finally, it sets the variable QUIT to T to indi- 
cate to DECLARATIVE that the sentence has been 
parsed and returns control to it. DECLARATIVE 
after checking that the variable QUIT has the 
value T, returns control to SENTENCE. This resets 
the variables RECOG, QUIT and STRUCT to NIL and 
begins to examine the next sentence. 
The calling sequence for the second sentence 
is identical to that for the first sentence except 
that the recognition of concepts is different. The 
passive frame for EXAM-SCORES does not contain any- 
thing about "registrar" nor about "supplies". 
DECLARATIVE has called ACTION-VERB to parse the 
verbal phrase. This has invoked DESCRIPTION to 
parse the object "data cards". STRUCT looks like: 
(SUBJ (REGISTRAR) ADV (ALSO) AV (SUPPLIES) OBJ ). 
ACTION-VERB is waiting for DESCRIPTION to parse 
"data cards" to fill the slot of OBJ. DESCRIPTION 
comes with card from "data cards," and invokes 
RECOGNIZE-CONCEPT. The specialist INPUT-SP is 
connected with card and it is again activated. 
This time the production rule that fires says: 
If what follows in the sentence is <univer- 
sal quatifier> + <D-VERB> or simply 
D-VERB then activate the function 
DESCRIPTIVE-VERB and pass it the 
recommendation of activating the 
INPUT-SUPERVISOR each time a complement 
is found. 
The pattern <universal quantifier> + <D-VERB> 
appears in the antecedent of the production rule 
because we want the system also to understand: 
"data cards each containing...". The rest of the 
sentence is parsed in a similar way to the first 
sentence. The INPUT-SUPERVISOR returns control to 
INPUT-SP that stacks "registrar" in the source slot 
of input. Finally the concept input for this prob- 
lem looks: 
INPUT CONSIST-OF (NAME (SCORES CARD (5))) 
SOURCE (INSTRUCTOR) 
(NAME ID-NUMBER P-COURSES) 
SOURCE (REGISTRAR) 
INPUT-TYPE (CARDS) 
If none of the concepts of a sentence are recog- 
nized - that is the sentence has been parsed and 
the variable RECOG is NIL - the system prints the 
sentence followed by a question mark to indicate 
that it could not make sense of it. That will 
happen if we take a sentence from a problem about 
checking~accounts and insert it in the middle of a 
problem about exam scores. The INPUT-SP and the 
INPUT-SUPERVISOR are the same specialists. The 
former overrides and guides the parser'when a con- 
cept is initially recognized, the latter plays the 
same role after the concept has been recognized. 
The following example illustrates how the INPUT- 
SUPERVISOR may furthermore override and guide the 
parser. 
The registrar also provides cards. 
Each card contains data including 
an identification number ... 
When processing the subject of the second sentence, 
INPUT-SP is activated. This tells the function 
DESCRIPTIVE-VERB to parse starting at "contains 
..." and to awaken the INPUT-SUPERVISOR when an 
object is parsed. The first object is "data" that 
has the marker IGENERIC that tells the INPUT-SUPER- 
VISOR that "data" can not be the value for the 
input. The INPUT-SUPERVISOR will examine the next 
concept looking for a D-VERB. Because that is the 
case, it will ask the routine DESCRIPTIVE-VERB to 
parse starting at "including an identification 
n~mber..." 
8. Example 2 
We will comment briefly on the first six 
sentences of the example in Fig. 2. We will name 
each sentence by quoting its beginning and its end. 
There is a specialist that has grouped the know- 
ledge about checking-accounts. This specialist, 
whose name is ACCOUNT-SP, will be invoked when the 
parser finds a concept that belongs to the slot of 
relevant concepts in the passive frame. The first 
sentence is: "A bank would like to produce... 
checking accounts". The OUTPUT-SP is activated by 
"like". When 0UTPUT-SP is activated by a verb with 
the feature of REQUEST, there are only two produc- 
tion rules that follow. One that considers that 
the next concept is an action verb, and another 
that looks for the pattern <REPORT + CONSIST 
D-VERB> (where "REPORT" is a semantic feature for 
"report," "list," etc.). In this case, the first 
rule is fired. Then ACTION-VERB is activated with 
the recommendation of invoking the OUTPUT-SUPERVI- 
SOR each time that an object is parsed. ACTION- 
VERB awakens the OUTPUT-SUPERVISOR with (RECORDS 
ABOUT (TRANSACTION)), Because "record" has the 
feature IGENERIC the OUTPUT-SUPERVISOR tries to 
redirect the parser by looking for a CONSIST 
D-VERB. Because the next concept is not a D-VERB, 
OUTPUT-SUPERVISOR sets RECOG to NIL and returns 
control to ACTION-VERB. This parses the adverbial 
phrase introduced by "during" and the prepositional 
phrase introduced by "with". ACTION-VERB parses 
the entire sentence without recognizing any rele- 
vant concept, except the identification of the 
frame that was done while processing "a bank". 
The second sentence "For each account the bank 
wants ... balance." is parsed in the following 
way. Although "account" belongs to slot of rele- 
vant concepts for this problem, it is skipped be- 
cause it is in a prepositional phrase that starts 
a sentence. The 0UTPUT-SP is activated by a 
40 
REQUEST type verb, "want". STRUCT looks like: 
(RECIPIENT (ACCOUNT UQ (EACH)) SUBJECT (BANK)). 
The production rule whose antecedent is <RECORD + 
CONSIST D-VERB> is fired. The DESCRIPTIVE-VERB 
function is asked to parse starting in "showing," 
and activate the OUTPUT-SUPERVISOR each time an 
object is parsed. The OUTPUT-SUPERVISOR inserts 
all objects in the CONSIST-OF slot of output, and 
returns control to the OUTPUT-SP that inserts the 
RECIPIENT, "account," in the CONSIST-OF slot of 
output and returns control. 
The next sentence is "The accounts and trans- 
actions ... as follows:" DECLARATIVE asks 
DESCRIPTION to parse the subject. Because account 
belongs to the relevant concepts of the passive 
frame, the ACCOUNT-SP specialist is invoked. There 
is nothing in STRUCT. When a topic specialist is 
invoked and the next word is a boolean conjunction, 
the specialist asks DESCRIPTION to get the next 
concept for it. If the concept does not belong to 
the llst of relevant concepts, the specialist sets 
RECOG to NIL and returns control. Otherwlse it 
continues examining the sentence. Because trans- 
action belongs to the slot of relevant concepts of 
the passive frame, ACCOUNT-SP continues in control. 
ACCOUNT-SP finds "for" and asks DESCRIPTION to 
parse the nominal phrase. ACCOUNT-SP ignores 
anything that has the marker HUMAN or TIME. 
Finally ACCOUNT-SP finds the verb, an APPEAR D-VERB 
and invokes the DESCRIPTIVE-VERB routine with the 
recommendation of invoking the ACCOUNT-SUPERVISOR 
each time a complement is found. The ACCOUNT- 
SUPERVISOR is awakened with card. This inserts 
"card" in the INPUT-TYPE slot of account and 
transaction and returns control to the DESCRIPTIVE- 
VERB routine. AS-SP (the routine for "as") is 
invoked next. This, after finding "follows" 
followed by ":," indicate to DESCRIPTIVE-VERB that 
the sentence has been parsed. ACCOUNT-SP returns 
control to DECLARATIVE and this, after checking 
that QUIT has the value T, returns control to 
SENTENCE. 
The next sentence is: "First will be a 
sequence of cards ... accounts." The INPUT-SP 
specialist is invoked. STRUCT looks like: (ADV 
(FIRST) EXIST ). "Sequence of cards" gives the 
concept card activating the INPUT-SP specialist. 
The next concept is a REPRESENT D-VERB. INPUT-SP 
activates the DESCRIPTIVE-VERB routine and asks it 
to activate the INPUT-SUPERVISOR each time an 
object is found. The INPUT-SUPERVISOR checks if 
the object belongs to the relevant concepts for 
checking accounts. If not, the ACCOUNT-SUPERVISOR 
will complain. That will be the case if the sen- 
tence is: "First will be a sequence of cards 
describing the students". Assume that the above 
sentence says: "First will be a sequence of cards 
consisting of an account number and the old 
balance." In that case, the INPUT-SP will activate 
also the INPUT-SUPERVISOR but because the verbal 
concept is a CONSIST D-VERB, the INPUT-SUPERVISOR 
will stack the complements in the slot for INPUT. 
Thus, what the supervisor specialists do depend 
on the verbal concept and what is coming after. 
The next sentence is: "Each account is 
described by ..., in dollars and cents." Again, 
the ACCOUNT-SP is activated. The next concept is 
a CONSIST D-VERB. ACCOUNT-SP assumes that it is 
the input for accounts and activates the 
DESCRIPTIVE-VERB function, and passes to it the 
recommendation of activating the INPUT-SUPERVISOR 
each time an object is parsed. The INPUT-SUPERVI- 
SOR is awakened with (NUMBERS CARDINAL (2)). Be- 
cause number is not an individual concept (like, 
say, 0 is) the INPUT-SUPERVISOR reexamines the sen- 
tence and finds ":," it then again asks to 
DESCRIPTIVE-VERB to parse starting at "the account 
number...". The INPUT-SUPERVISOR stacks the com- 
plements in the input slot of the concept that is 
being described: account. 
The next sentence is: "The last account is 
followed by ... to indicate the end of the list." 
The ACCOUNT-SP is invoked again. The following 
production rule is fired: If the ordinal "last" 
is modifying "account" and the next concept is a 
SPATIAL D-VERB then activate the END-OF-DATA 
specialist. This assumes control and asks 
DESCRIPTIVE-VERB to parse starting at "followed by" 
with the usual recommendation of awakening the END- 
OF-DATA supervisor when a complement is found, and 
the recommendation of ignoring a PURPOSE clause if 
the concept is end-of-list or end-of-account. The 
END-OF-DATA is awakened with "dummy-account". 
Because "dtumny-account" is not an individual con- 
cept, the END-OF-DATA supervisor reexamines the 
sentence expecting that the next concept is a 
CONSIST D-VERB. It finds it, and redirects the 
parser by asking the DESCRIPTIVE-VERB to parse 
starting in "consisting of two zero values." The 
END-OF-DATA is awakened with "(ZERO CARD (2))". 
Because this time the object is an individual 
concept, the END-OF-DATA supervisor inserts it in- 
to the END-OF-DATA slot of the concept being des- 
cribed: account. 
9. Conclusion 
LLULL was running in the Dec 20/20 under UCI 
Lisp in the Department of Computer Science of the 
Ohio State University. It has been able to under- 
stand ten programming problems taken verbatim from 
text books. A representative example can be found 
in Fig. 2. After the necessary modifications, the 
system is presently running in a VAXlI/780 under 
Franz Lisp. We are now in the planning stage of 
extensively experimenting with the system. We 
predict that the organization that we have proposed 
will make relatively simple to add new problem 
areas. Assume that we want LLULL to understand 
programming problems about roman numerals, say. 
We are going to find uses of verbs, prepositions, 
etc. that our parser will not be able to handle. 
We will integrate those uses in the parser. On 
top of that we will build some conceptual special- 
ists that will have inferential knowledge about 
roman numerals, and a thematic frame that will hold 
structural knowledge about roman numerals. We are 
presently following this scheme in the extension of 
LLULL. In the next few months we expect to fully 
evaluate our ideas. 
I0. A Computer Run 
41 
The example below has been taken verbatim 
from Conway and GriPs (1975). Some notes about 
the output for this problem are in order. 
i) "SPEC" is a semantic feature that stands for 
specification. If it follows a concept,- it means 
that the concept is being further specified or 
described. The semantic feature "SPEC" is followed 
by a descriptive verb or adjective, and finally it 
comes the complement of the specification in paren- 
theses. In the only instance in which the descrip- 
tive predicate does not follow the word SPEC is in 
expressions like "the old balance in dollars and 
cents". Those expressions have been treated as a 
special construction. 2) All direct objects 
connected by the conjunction "or" appear enclosed 
in parentheses. 3) "REPRESENT" is a semantic 
marker and stands for a REPRESENT D-VERB. 
4) Finally "(ZERO CARD (3))" means three zeros. 
(A BANK WOULD LIKE TO PRODUCE RECORDS OF THE 
TRANSACTIONS DURING AN ACCOUNTING PERIOD IN 
CONNECTION WITH THEIR CHECKING ACCOUNTS. FOR EACH 
ACCOUNT THE BANK WANTS A LIST SHOWING THE BALANCE 
AT THE BEGINNING OF THE PERIOD, THE NUMBER OF 
DEPOSITS AND WITHDRAWALS, AND THE FINAL BALANCE. 
THE ACCOUNTS AND TRANSACTIONS FOR AN ACCOUNTING 
PERIOD WILL BE GIVEN ON PUNCHED CARDS AS FOLLOWS: 
FIRST WILL BE A SEQUENCE OF CARDS DESCRIBING THE 
ACCOUNTS. EACH ACCOUNT IS DESCRIBED BY TWO NUM- 
BERS: THE ACCOUNT NUMBER (GREATER THAN 0), AND 
THE ACCOUNT BALANCE AT THE BEGINNING OF THE PERIOD, 
IN DOLLARS AND CENTS. %~E LAST ACCOUNT IS FOLLOWED 
BY A DUMMY ACCOUNT CONSISTING OF TWO ZERO VALUES 
TO INDICATE THE END OF THE LIST. THERE WILL BE AT 
MOST 200 ACCOUNTS. FOLLOWING THE ACCOUNTS ARE THE 
TRANSACTIONS. EACH TRANSACTION IS GIVEN BY THREE 
NUMBERS: THE ACCOUNT NUMBER, A i OR -I (INDICATING 
A DEPOSIT OR WITHDRAWAL, RESPECTIVELY), AND THE 
TRANSACTION AMOUNT, IN DOLLARS AND CENTS. THE LAST 
REAL TRANSACTION IS FOLLOWED BY A DUMMY TRANSACTION 
CONSISTING OF THREE ZERO VALUES.) 
Figure 2 A Programming Problem 
OUTPUT CONSIST-OF (ACCOUNT OLD-BAL DEPOSITS 
WITHDRAWALS FINAL-BAL) 
ACCOUNT INPUT (ACCOUNT-NUMBER SPEC GREATER (0) 
OLD-BAL SPEC (DOLLAR-CENT)) 
INPUT-TYPE (CARDS) 
END-OF-DATA ((ZERO CARD (2))) 
NUMBER-OF-ACCOUNTS (200) 
TRANSACTION INPUT (ACCOUNT-NUMBER (1 OR -i) 
REPRESENT 
(DEPOSIT OR WITHDRAWAL) 
TRANS-AMOUNT SPEC (DOLLAR-CENT)) 
INPUT-TYPE (CARDS) 
END-OF-DATA ((ZERO CARD (3))) 
Figure 3 System Output for Problem in Figure 2 
ACKNOWLEDGEMENTS 
This research was supported by the Air Force 
Office of Scientific Research under contract 
F49620-79-0152, and was done in part while the 
author was a member of the AI group at the Ohio 
State University. 
I would llke to thank Amar Mukhopadhyay for 
reading and providing constructive comments on 
drafts of this paper, and Mrs. Robin Cone for her 
wonderful work in typing it. 
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43 
