KNOWLEDGE REPRESENTATION FOR COMMONSENSE REASONING 
WITH TEXT 
Kathleen Dahlgren 
Joyce McDowell 
IBM Los Angeles Scientific Center 
Edward P. Stabler, Jr.l 
University of Western Ontario 
1 INTRODUCTION 
1.1 NAIVE SEMANTICS 
The reader of a text actively constructs a rich picture of 
the objects, events, and situation described. The text is 
a vague, insufficient, and ambiguous indicator of the 
world that the writer intends to depict. The reader 
draws upon world knowledge to disambiguate and clar- 
ify the text, selecting the most plausible interpretation 
from among the (infinitely) many possible ones. In 
principle, any world knowledge whatsoever in the read- 
er's mind can affect the choice of an interpretation. Is 
there a level of knowledge that is general and common 
to many speakers of a natural language? Can this level 
be the basis of an explanation of text interpretation? 
Can it be identified in a principled, projectable way? 
Can this level be represented for use in computational 
text understanding? We claim that there is such a level, 
called naive semantics (NS), which is commonsense 
knowledge associated with words. Naive semantics 
identifies words with concepts, which vary in type. 
Nominal concepts are categorizations of objects based 
upon naive theories concerning the nature and typical 
description of conceptualized objects. Verbal concepts 
are naive theories of the implications of conceptualized 
events and states. 2 Concepts are considered naive be- 
cause they are not always objectively true, and bear 
only a distant relation to scientific theories. An informal 
example of a naive nominal concept is the following 
description of the typical lawyer. 
1. If someone is a lawyer, typically they are male or 
female, well-dressed, use paper, books, and brief- 
cases in their job, have a high income and high 
status. They are well-educated, clever, articulate, 
and knowledgeable, as well as contentious, ag- 
gressive, and ambitious. Inherently lawyers are 
adults, have gone to law school, and have passed 
the bar. They practice law, argue cases, advise 
clients, and represent them in court. Conversely, 
if someone has these features, he/she probably is a 
lawyer. 
In the classical approach to word meaning, the aim is to 
find a set of primitives that is much smaller than the set 
of words in a language and whose elements can be 
conjoined in representations that are truth-conditionally 
adequate. In such theories "bachelor" is represented as 
a conjunction of primitive predicates. 
2. bachelor(X) ¢~ adult(X) & human(X) & 
male(X) & unmarried(X) 
In such theories, a sentence such as (3) can be given 
truth conditions based upon the meaning representation 
of "bachelor," plus rules of compositional semantics 
that map the sentence into a logical formula that asserts 
that the individual denoted by "John" is in the set of 
objects denoted by "bachelor." 
3. John is a bachelor. 
The sentence is true just in case all of the properties in 
the meaning representation of "bachelor" (2) are true of 
"John." This is essentially the approach in many com- 
putational knowledge representation schemes such as 
KRYPTON (Brachman et al. 1985), approaches follow- 
ing Schank (Schank and Abelson 1977), and linguistic 
semantic theories such as Katz (1972) and Jackendoff 
(1985). 
Smith and Medin (1981), Dahlgren (1988a), Johnson- 
Laird (1983), and Lakoff (1987) argue in detail that all of 
these approaches are essentially similar in this way and 
all suffer from the same defects, which we summarize 
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Computational Linguistics, Volume 15, Number 3, September 1989 149 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
briefly here. Word meanings are not scientific theories 
and do not provide criteria for membership in the 
categories they name (Putnam 1975). Concepts are 
vague, and the categories they name are sometimes 
vaguely defined (Labov 1973; Rosch et al. 1976). Mem- 
bership of objects in categories is gradient, while the 
classical approach would predict that all members share 
full and equal status (Rosch and Mervis 1975). Not all 
categories can be decomposed into primitives (e.g., 
color terms). Exceptions to features in word meanings 
are common (most birds fly, but not all) (Fahlman 1979). 
Some terms are not intended to be used truth-condition- 
ally (Dahlgren 1988a). Word meanings shift in unpre- 
dictable ways based upon changes in the social and 
physical environment (Dahlgren 1985b). The classical 
theory also predicts that fundamentally new concepts 
are impossible. 
NS sees lexical meanings as naive theories and 
denies that meaning representations provide truth con- 
ditions for sentences in which they are used. NS ac- 
counts for the success of natural language communica- 
tion, given the vagueness and inaccuracy of word 
meanings, by the fact that natural language is anchored 
in the real world. There are some real, stable classes of 
objects that nouns are used to refer to, and mental 
representations of their characteristics are close enough 
to true, enough of the time, to make reference using 
nouns possible (Boyd 1986). Similarly, there are real 
classes of events which verbs report, and mental repre- 
sentations of their implications are approximately true. 
The vagueness and inaccuracy of mental representa- 
tions requires non-monotonic reasoning in drawing in- 
ferences based upon them. Anchoring is the main 
explanation of referential success, and the use of words 
for imaginary objects is derivative and secondary. 
NS differs from approaches that employ exhaustive 
decompositions into primitive concepts which are sup- 
posed to be true of all and only the members of the set 
denoted by lawyer. NS descriptions are seen as heuris- 
tics. Features associated with a concept can be overrid- 
den or corrected by new information in specific cases 
(Reiter 1980). NS accounts for the fact that while 
English speakers believe that an inherent function of a 
lawyer is to practice law, they are also willing to be told 
that some lawyer does not practice law. A non-prac- 
ticing lawyer is still a lawyer. The goal in NS is not to 
find the minimum set of primitives required to distin- 
guish concepts from each other, but rather, to represent 
a portion of the naive theory that constitutes the cogni- 
tive concept associated with a word. NS descriptions 
include features found in alternative approaches, but 
more as well. The content of features is seen as essen- 
tially limitless and is drawn from psycholinguistic stud- 
ies of concepts. Thus, in NS, featural descriptions 
associated with words have as values not primitives, but 
other words, as in Schubert et al. (1979). 
In NS, the architecture of cognition that is assumed 
is one in which syntax, compositional semantics, and 
Parser 
Compositkmal 
SemontTca 
X 
w 
Compositional \[ 
Augmentation of the Discourse 
Naive Inference 
Figure 1. Components of Grammar in NS. 
naive semantics are separate components with unique 
representational forms and processing mechanisms. 
Figure l illustrates the components. The autonomous 
syntactic component draws upon naive semantic infor- 
mation for problems such as prepositional phrase at- 
tachment and word sense disambiguation. Another au- 
tonomous component interprets the compositional 
semantics and builds discourse representation structures 
(DRSs) as in Kamp (1981) and Asher (1987). Another 
component models naive semantics and completes the 
discourse representation that includes the implications 
of the text. All of these components operate in parallel 
and have access to each other's representations when- 
ever necessary. 
1.2 THE KT SYSTEM 
Naive semantics is the theoretical motivation for the KT 
system under development at the IBM Los Angeles 
Scientiific Center by Dahlgren, McDowell, and others. 3 
The heart of the system is a commonsense knowledge 
base with two components, a commonsense ontology 
and databases of generic knowledge associated with 
lexical items. The first phase of the project, which is 
nearly complete, is a text understanding and query 
system. In this phase, text is read into the system and 
parsed by the MODL parser (McCord 1987), which has 
very wide coverage. The parse is submitted to a module 
(DISAMBIG) that outputs a logical structure which 
reflects the scope properties of operators and quantifi- 
ers, correct attachment of post-verbal adjuncts, and 
selects, word senses. This is passed to a semantic 
translator whose output (a DRS) is then converted to 
first-order logic (FOL). We then have the text in two 
different semantic forms (DRS and FOL), each of which 
has its advantages and each of which is utilized in the 
system in different ways. Queries are handled in the 
same way as text. Answers to the queries are obtained 
either by matching to the FOL textual database or to the 
commonsense databases. However, the commonsense 
knowledge is accessed at many other stages in the 
processing of text and queries, namely in parse disam- 
biguafion, in lexical retrieval, anaphora resolution, and 
in the construction of the discourse structure of the 
entire text. 
150 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
The second phase of the project, which is at present 
in the research stage, will be to use the commonsense 
knowledge representations and the textual database to 
guide text selection. We anticipate a system, NewSe- 
lector, which, given a set of user profiles, will distribute 
textual material in accordance with user interests, thus, 
in effect, acting as an automatic clipping service. Our 
target text is the Wall Street Journal. The inferencing 
capabilities provided by the commonsense knowledge 
will allow us to go well beyond simple keyword search. 
The theoretical underpinnings and practical work on 
the KT system have been reported extensively else- 
where, in conference papers (Dahlgren and McDowell 
1986a, 1986b; McDowell and Dahlgren 1987) and in a 
book (Dahlgren 1988a). Since the publication of those 
works, a number of significant additions and modifica- 
tions have been made to the system. The intended focus 
of this paper is this new work. However, in order to 
make this accessible to readers unfamiliar with our 
previous reports, we present in Section 2 an overview of 
the components of the present system. (Readers famil- 
iar with our system can skip Section 2). In the remaining 
sections on new work we have emphasized implemen- 
tation, because this paper is addressed to the computa- 
tional linguistics community: in Section 3, the details of 
disambiguation procedures that use the NS representa- 
tions and in Section 4 the details of the query system. 
Finally, Section 5 discusses work in progress regarding 
discourse and naive semantics. 
2 OVERVIEW or THE KT SYSTEM 
2.1 KNOWLEDGE REPRESENTATION 
2.1.1 THE ONTOLOGY 
Naive theories associated with words include beliefs 
concerning the structure of the actual world and the 
significant "joints" in that structure. People have the 
environment classified, and the classification scheme of 
a culture is reflected in its language. Since naive seman- 
tics is intended as a cognitive model, we constructed the 
naive semantic ontology empirically, rather than intu- 
itix;ely. We studied the behavior of hundreds of verbs 
and determined selectional restrictions, which are con- 
straints reflecting the naive ontology embodied in En- 
glish. We also took into account psychological studies 
of classification (Keil 1979), and philosophical studies of 
epistemology (Strawson 1953). 
2.1.1.1 MATHEMATICAL PROPERTIES OF THE ONTOLOGY 
The ontology has several properties which distinguish it 
from classical taxonomies. It is a directed acyclic graph, 
rather than a binary tree, because many concepts have 
more than two subordinate concepts (Rosch et al. 1976). 
FISH, BIRD, MAMMAL, and so on, are subordinates 
of VERTEBRATE. It is a directed graph rather than a 
tree, because it handles cross-classification. Cross-clas- 
sification is justified by contrasts between individual 
and collective nouns such as "cow" and "herd." This 
ENTITY 
ABSTRACT 
NUMERICAL 
REAL 
PHYSICAL --* 
NON-STATIONARY --* 
COLLECTIVE --* 
TEMPORAL ---> 
RELATIONAL --~ 
EVENT --* 
Table 1. 
(ABSTRACT v REAL) & (INDIVIDUAL v 
COLLECTIVE) 
IDEAL v PROPOSITIONAL v 
NUMERICAL v IRREAL 
NUMBER v MEASURE 
(PHYSICAL v TEMPORAL v SENTIENT) 
& (NATURAL v SOCIAL) 
(STATIONARY v NONSTATIONARY) & 
(LIVING v NONLIVING) 
(SELFMOVING v NONSELFMOVING) 
MASS v SET v STRUCTURE 
RELATIONAL v NONRELATIONAL 
(EVENT v STATIVE) & (MENTAL v 
EMOTIONAL v NONMENTAL) 
(GOAL v NONGOAL) & (ACTIVITY v 
ACCOMPLISHMENT v ACHIEVEMENT) 
The Ontological Schema 
implies that cognitively there are essentially parallel 
ontological schemas for individuals and collectives. 
Thus we have the parallel ontology fragments in Figure 
2. Table 2 illustrates cross-classification at the root of 
the ontology, where ENTITY cross-classifies as either 
REAL or ABSTRACT, and as either INDIVIDUAL or 
COLLECTIVE. Cross-classification is handled as in 
McCord (1985, 1987). 
INDIVIDUAL COLLECTIVE 
ENTITY ENTITY 
ABSTRACT REAL ABSTRACT REAL / / 
P~SICAL, N~U RAI_, SE ~M~ING PH~IC~,~TURAL,SE~VING / / 
ANIMAL FAUNA / / 
COW HERD 
Figure 2. Parallel Portions of the Ontology. 
INDIVIDUAL COLLECTIVE 
REAL cow herd 
ABSTRACT idea book 
Table 2. Entity Node Cross Classification 
Multiple attachments of instantiations to leaves is 
possible. For example, an entity, John, is both a HU- 
MAN with the physical properties of a MAMMAL, and 
is also a PERSON, a SENTIENT. As a SENTIENT, 
John can be the subject of mental verbs such as think 
and say. Institutions are also SENTIENTs, so that the 
SENTIENT node reflects English usage in pairs like (4). 
4. John sued Levine. The government sued Levine. 
Computational Linguistics, Volume 15, Number 3, September 1989 151 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
On the other hand, John, as a HUMAN, is like animals, 
and has physical properties. Both John and a cow can 
figure as subjects of verbs like "eat" and "weigh." 
Multiple attachment was justified by an examination of 
the texts. It was found that references to human beings 
in text, for example, deal with them either as persons 
(SENTIENTs) or as ANIMALs (physiological beings), 
but rarely as both at the same time. 
2.1.1.2 ONTOLOGICAL CATEGORIES 
The INDIVIDUAL/COLLECTIVE cut was made at 
the level of ENTITY (the highest level) because all 
types of entities are conceived individually or in collec- 
tions. COLLECTIVE breaks into sets of identical mem- 
bers (herd, mob, crowd, fleet), masses that are con- 
ceived as stuff (sand, water), and structures where the 
members have specified relations, such as in institutions 
(school, company, village). Leaf node names, such as 
ANIMAL and FAUNA, are shorthand for collections of 
categories inherited from dominating nodes and by 
cross-classification. Thus "cow" and "herd" share all 
categories except that "cow" is an INDIVIDUAL term 
and "herd" is a COLLECTIVE term. 
The REAL node breaks into the categories PHYSI- 
CAL, TEMPORAL, and SENTIENT, and also NAT- 
URAL and SOCIAL. Entities (or events) that come into 
being (or take place) naturally must be distinguished 
from those that arise through some sort of social inter- 
vention. Table 3 illustrates the assignment of example 
words under the REAL cross-classification. 
INDIVIDUAL COLLECTIVE 
NATURAL SOCIAL NATURAL SOCIAL 
PHYSICAL rock knife sand fleet 
SENTIENT man programmer mob clinic 
TEMPORAL earthquake party winter epoch 
Table 3. Attachment of Nouns under REAL 
The SENTIENT/PHYSICAL distinction is placed high 
because in commonsense reasoning, the properties of 
people and things are very different. Verbs select for 
SENTIENT arguments or PHYSICAL arguments, as 
illustrated in (5). Notice also, that as a physical object, 
an individual entity like John can be the subject both of 
verbs that require physical subjects and those that 
require SENTIENT subjects, as in (6). 
5. The lawyer/the grand jury indicted Levine. 
*The cow indicted Levine. 
6. John fell. 
John sued Levine. 
The cow fell. 
We make the SENTIENT/NON-SENTIENT distinc- 
tion high up in the hierarchy for several reasons. 
Philosophically, the most fundamental distinction in 
epistemology (human knowledge) is arguably that be- 
tween thinking and non-thinking beings (Strawson 
1953). Psychology has shown that infants are able to 
distinguish humans from all other objects and they 
develop a deeper and more complex understanding of 
humans than of other objects (Gelman and Spelke 1981). 
In the realm of linguistics, a class of verbs selects for 
persons, roles, and institutions as subjects or objects. 
Thus the SENTIENT distinction captures the similarity 
between persons and institutions or roles. There is a 
widespread lexical ambiguity between a locational and 
institutional reading of nouns, which can be accounted 
for by the SENTIENT distinction, as in (7). 
7. The court is in the center of town. 
The court issued an injunction. 
The NATURAL/SOCIAL distinction also was placed 
high in the hierarchy. Entities (including events) that are 
products of society, and thereby have a social function, 
are viewed as fundamentally different from natural 
entities in the commonsense conceptual scheme. The 
distinction is a basic one psychologically (Miller 1978; 
Gelman and Spelke 1981). SOCIAL entities are those 
that come into being only in a social or institutional 
setting, with "institution" being understood in the 
broadest sense, for instance family, government, edu- 
cation,, warfare, organized religion, etc. 
2.1.1.3 CONSTRUCTION OF THE ONTOLOGY 
The ontological schema was constructed to handle the 
selectional restrictions of verbs in 4,000 words of geog- 
raphy text and 6,000 words of newspaper text. These 
were arranged in a hierarchical schema. The hierarchy 
was examined and modified to reflect cognitive, philo- 
sophical, and linguistic facts, as described above. It was 
pruned to make it as compact as possible. We mini- 
mized empty terminal nodes. A node could not be part 
of the ontology unless it systematically pervaded some 
subhierarchy. Distinctions found in various places were 
relegated to feature status. The full ontology with 
examples may be found in Dahlgren (1988). 
2.1.1.4 VERBS 
In KT, verbs are attached to the main ontology at the 
node TEMPORAL because information concerning the 
temporality of situations described in a sentence is 
encoded on the verb as tense and because the relations 
indicated by verbs must be interpreted with respect to 
their location in time in order to properly understand the 
discourse structure of a text. Thus the ontology implies 
that events are real entities, and that linguistic, not 
conceptual, structure distinguishes verbalized from 
nominalized versions of events and states. 
We view the cognitive structure of the concepts 
associated with nouns and verbs as essentially different. 
Non-derived nouns in utterances refer to objects. Lex- 
ical nouns name classes of entities that share certain 
features. Verbs name classes of events and states, but 
152 Computational Linguistics, Volume 15, Number 3, September 1989 
Kath|een Dahlgren, Joyce McDoweli, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
these do not share featural descriptions. Psychologi- 
cally, verbs are organized around goal orientation and 
argument types (Huttenlocher and Lui 1979; Graesser 
and Hopkinson 1987). 
The primary category cut at the node TEMPORAL is 
between nouns, which name classes of entities, in this 
case temporal entities like "party," "hurricane," and 
"winter," and verbs, which indicate relations between 
members of these nominal classes, like "hit," "love," 
"remember." We attach temporal nouns to the TEM- 
PORAL/NON-RELATIONAL node and verbs to the 
TEMPORAL/RELATIONAL node. Many nouns are, 
of course, relational, in the sense that "father" and 
"indictment" are relational. Our node RELATIONAL 
does not carry this intuitive sense of relational, but 
instead simply indicates that words attached here re- 
quire arguments for complete interpretation. So, while 
"father" is relational, it is possible to use "father" in a 
text without mentioning the related entity. But verbs 
require arguments (usually overtly, but sometimes un- 
derstood, as in the case of commands) for full interpre- 
tation. Nominalizations like "indictment" are a special 
case. In our system, all deverbal nominalizations are so 
marked with a pointer to the verb from which they are 
derived. Subsequent processing is then directed to the 
verb, which, of course, is attached under TEMPORAL, 
RELATIONAL. 
2.1.1.5 THE VENDLER CLASSIFICATION 
One basis of the relational ontology is the Vendler 
(1967) classification scheme, which categorizes verbs 
into aspectual classes (see Dowty 1979). According to 
this classification, RELATIONAL divides into EVENT 
or STATIVE, and EVENT divides into ACTIVITY, 
ACHIEVEMENT, or ACCOMPLISHMENT. Vendler 
and others (particularly Dowty 1979) have found the 
following properties, which distinguish these classes. 
8. STATIVE and ACHIEVEMENT verbs may not 
appear in the progressive, but may appear in the 
simple present. 
ACTIVITY and ACCOMPLISHMENT verbs 
may appear in the progressive and if they appear in 
the simple present, they are interpreted as describ- 
ing habitual or characteristic states. 
ACCOMPLISHMENTs and ACHIEVEMENTs 
entail a change of state associated with a terminus 
(a clear endpoint). STATIVEs ("know") and AC- 
TIVITYs ("run") have no well-defined terminus. 
ACHIEVEMENTs are punctual (John killed 
Mary) while ACCOMPLISHMENTs are gradual 
(John built a house). 
STATEs and ACTIVITYs have the subinterval 
property (cf. Bennett and Partee 1978). 
Table 4 summarizes these distinctions. There are sev- 
eral standard tests for the Vendler (1967) system which 
can be found in Dowty (1979) and others and which we 
apply in classification. 
The Vendler classification scheme is actually more 
accurately a classification of verb phrases than verbs. 
KT handles this problem in two ways. First, in sentence 
processing we take into account the arguments in the 
verb phrase as well as the verb classification to deter- 
mine clause aspect, which can be any of the Vendler 
classes. Second, we classify each sense of a verb 
separately. 
Progressives 
Terminus 
Change of State 
Subinterval 
Property 
Activity Accomplish- 
ment 
run, think build a house, 
read a novel 
+ + 
- + 
- Gradual 
+ 
Achievement 
recognizejqnd 
+ 
Punctual 
State 
have, want 
+ 
Table 4. The Vendler Verb Classification Scheme 
The other nodes in the relational ontology are motivated 
by the psycholinguistic studies noted above. The MEN- 
TAL/NONMENTAL/EMOTIONAL distinction is made 
at the highest level for the same reasons that led us to 
place SENTIENT at a high level in the main ontology. 
All EVENTs are also cross-classified as GOAL ori- 
ented or not. This is supported by virtually every 
experimental study on the way people view situations, 
i.e., GOAL orientation is the most salient property of 
events and actions. For example, Trabasso and Van den 
Broek (1985) find that events are best recalled which 
feature the goals of individuals and the consequences of 
goals and Trabasso and Sperry (1985) find that the 
salient features of events are goals, antecedents, conse- 
quences, implications, enablement, causality, motiva- 
tion, and temporal succession and coexistence. This 
view is further supported by Abbott, Black, and Smith 
(1985) and Graesser and Clark (1985). NONGOAL, 
ACCOMPLISHMENT is a null category because AC- 
COMPLISHMENTS are associated with a terminus 
and thus inherently GOAL oriented. On the other hand 
NONGOAL, ACHIEVEMENT is not a null category 
because the activity leading up to an achievement is 
always totally distinct from the achievement itself. 
SOCIAL, NONGOAL, ACTIVITY is a sparse cate- 
gory. 
Cross-classifications inherited from the TEMPORAL 
node are SOCIAL/NATURAL and INDIVIDUAL/ 
COLLECTIVE. The INDIVIDUAL/COLLECTIVE 
distinction is problematical for verbs, because all events 
can be viewed as a collection of an infinitude of sub- 
events. 
2.1.2 GENERIC KNOWLEDGE 
Generic descriptions of the nouns and verbs were drawn 
from psycholinguistic data to the extent possible. In a 
typical experiment, subjects are asked to freelist fea- 
tures "characteristic" of and common to objects in 
Computational Linguistics, Volume 15, Number 3, September 1989 153 
Kathleen Dahlgren, Joyce McDoweH, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
categories such as DOG, LEMON, and SECRETARY 
(Rosch et al. 1976; Ashcraft 1976; Dahlgren 1985a). The 
number of subjects in such an experiment ranges from 
20 to 75. Any feature that is produced in a freelisting 
experiment by several subjects is likely to be shared in 
the relevant subpopulation (Rosch 1975; Dahlgren 
1985a). Features that were freelisted by at least one-fifth 
of the subjects are chosen for a second experiment, in 
which different subject s are asked to rate the features 
for typicality. Those features rated as highly typical by 
the second group can be considered a good first approx- 
imation to the content of the cognitive structures asso- 
ciated with the terms under consideration. The number 
of features shared in this way for a term averaged 15. 
The generic knowledge in the KT system is contained 
in two generic data bases, one for nouns and one for 
verbs. There is a separate entry for each sense of each 
lexical item. The content of the entries is a pair of lists 
of features drawn from the psycholinguistic data (as 
described above) or constructed using these data as a 
model. A feature is, informally, any bit of knowledge 
that is associated with a term. In informal terms, these 
can be any items like "wears glasses" (programmer), 
"is red" (brick), or "can't be trusted" (used-car sales- 
man). For each entry, the features are divided into two 
lists, one for typical features and one for inherent 
features. For example, a brick is typically red, but blood 
is inherently red. Together the lists comprise the entry 
description of the term that heads the entry. 
The source of descriptions of social roles were data 
collected by Dahlgren (1985a). For physical objects we 
used generic descriptions from Ashcraft (1976), includ- 
ing raw data generously supplied by the author. An 
informal conceptualization for "lawyer" was shown in 
(1). The corresponding generic description is shown in 
(9). Features of the same feature type within either the 
inherent or typical list are AND'ed or OR'ed as re- 
quired. Some features contain first-order formulas like 
the conditions in discourse representations. For exam- 
ple, one function feature has (advise(E,noun,Y) & 
client(Y) & regarding(E,Z) & law(Z)). The first 
argument of the predicate advise is an event reference 
marker. This event is modified in the regarding predi- 
cate. The second argument of advise is instantiated in 
the processing as the same entity that is predicated as 
being in the extension of the noun generically described 
in the representation, in this case, "lawyer." 
9. lawyer( 
{behavior( contentious ),appearance(well-dressed), 
status(htgh)~mcome(hlgh), 
sex(male ),sex(feinale ),tools(paper ),tools(books ), 
tools(briefcase), 
function(negotlate(*jxoun,Y) ~¢ settlement(Y)), 
internal_trait( ambitious);tnternaLtrait( articulate ), 
internal_trait( aggressive ), 
internal_trait(clever), 
interns\]_trait (knowledgeable) }, 
{age( adult )'educatl°n(law-sch°°l)' 
leg aLreq(pass(*jmun~) & bar(X)) 
154 
fmlctlon(practlce(*,noun,Y) & law(Y)), 
functlon(advise(E,noun,Y) & client(Y) & 
regarcUng(E,Z) & law(Z)), 
ikmction(represent(E,noun,Y) & client(Y) & 
ln(E,Z) & court(Z)), 
function(argue(*jloun,Y) & case(Y))}). 
The entire set of features for nouns collected in this way 
so far sort into 38 feature types. These are age, agent, 
appearance, association, behavior, color, construc- 
tion, content, direction, duration, education, exem- 
plar, experienced-as, in-extension-of, frequency, 
function, goal, habitat, haspart, hasrole, hierarchy, 
internal-trait, legal-requirement, length, level, loca- 
tion, manner, material, name, object, odor, opera- 
tion, owner, partof, physiology, place, processing, 
propagation, prototype, relation, requirement, 
rolein, roles, sex, shape, size, source, speed, state, 
status, strength, structure, taste, texture, time. A 
given feature type can be used in either typical or 
inherent feature lists. Since these are not primitives, we 
expect the list of feature types to expand as we enlarge 
the semantic domain of the system. 
There is a much smaller set of feature types for 
verbs. We were guided by recent findings in the psy- 
cholinguistic literature which show that the types of 
information that subjects associate with verbs are sub- 
stantially different from what they associate with nouns. 
Huttenlocher and Lui (1979) and Abbott, Black, and 
Smith (1985) in particular have convincingly argued that 
subjects conceive of verbs in terms of whether or not 
the activities they describe are goal oriented, the causal 
and temporal properties of the events described, and the 
types of entities that can participate as arguments to the 
verb. For the actual feature types, we adapted the 
findings of Graesser and Clark (1985), whose research 
focused on the salient implications of events in narra- 
tives. A small number of feature types is sufficient to 
represent the most salient features of events. These can 
be thought of as answers to questions about the typical 
event described by the verb that heads the entry. In 
addition, selectional restrictions on the verb are also 
encoded as a feature type. Feature types for verbs are 
cause, goal, what.enabled, what.happened_next, 
consequence_oLevent, where, when, implies, how, 
selectioD..\]-restriction. An example of a generic entry 
for verbs follows. 
10. 
~v( 
{wha~enabled(can( 
a~ox~l(mabj,obJ)) ), 
how(with(X) & money(X)), 
where(in(Y) & store(Y)), 
cause(need~bJ,obj) ), 
what_happened-next(use(subJ,obJ) )}, 
{goal( own( subJ ,obJ ) ), 
consectuence_oLevent(own( ( subJ ,obJ ) ), 
selectionsLrestrtction(sentient(subJ )), 
implies(merchandise( obJ ) )} ). 4 
if someone buys something, 
typlca£ly he can afford it, 
he uses money, 
he buys it in a store, 
he needs it, 
and later he uses it. 
Inherently, his goal is to 
own it, 
and after buying it, he does 
OwTt it. 
The buyer is sentient and 
what is bought is 
merchandise. 
Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
Naive Semantic representations of generic knowledge 
contain fifteen or more pieces of information per word, 
relatively more than required by other theories. The 
magnitude of the lexicon is counterbalanced by con- 
straints that naturally obtain in the generic knowledge. 
Study of the protocols of subjects in prototype experi- 
ments reveals that people conceive of objects in con- 
strained patterns of feature types. For example, animals 
are conceived in terms of physical and behavioral 
properties, while social roles are conceived in terms of 
functional and relational properties. Thus not all feature 
types occur in the representations of all nouns (or 
verbs). The pattern of features relevant to each node in 
the ontology is called a Kind Type. Each feature is 
classified by type as a COLOR, SIZE, FUNCTION, 
INTERNAL TRAIT or other. At each node, only 
certain feature types are applicable. Features at lower 
nodes inherit feature type constraints from the nodes 
above them in the ontology. For instance, any node 
under SOCIAL may have certain feature types, and any 
node under ROLE may have those feature types inher- 
ited from SOCIAL, as well as further feature types. 
Examples contrasting "elephant" and "lawyer" are 
shown in Tables 5 and 6. 
Node in Feature types associated Feature values for Ontology with the node elephant 
ENTITY haspart trunk 
haspart 4 legs 
partof herd 
PHYSICAL color grey 
size vehiclesized 
texture rough 
LIVING propagation live births 
habitat jungle 
ANIMAL sex male or female 
behavior lumbers 
behavior eats grass 
Table 5. Animal Kind Type 
A lexical augmentation facility is used to create 
generic entries. This facility exploits the fact that pos- 
sible feature types for any term are constrained by the 
ontological attachment of the term, by the Kind Type to 
which they belong (Dahlgren & McDowell 1986a; Dahl- 
gren 1988a). For example, it is appropriate to encode 
the feature type "behavior" for "dog" but not for 
"truck." Similarly, it is appropriate to encode the 
feature type "goal" for "dig" but not for "fall." The 
lexical augmentation facility presents the user with 
appropriate choices for each term and then converts the 
entries to a form suitable for processing in the system. 
2.2 TEXT INTERPRETATION ARCHITECTURE 
2.2.1 PARSER 
The overall goal of the KT project is text selection 
based on the extraction of discourse relations guided by 
Node in Feature types associated Feature values for Ontology with the node lawyer 
SOCIAL function types 
function practice law 
function argue cases 
function advise clients 
function represent clients in court 
requirement pass bar 
appearance well-dressed 
SENTIENT internaltrait friendly 
education law school 
internaltrait clever 
internaltrait articulate 
internaltrait contentious 
sex male 
sex female 
ROLE relation high status 
income high 
tools books 
tools briefcases 
Table 6. Social Role Kind Type 
naive semantic representations. This goal motivated the 
choice of DRT as the compositional semantic formalism 
for the project. The particular implementation of DRT 
which we use assumes a simple, purely syntactic parse 
as input to the DRS construction procedures (Wada and 
Asher 1986). Purely syntactic parsing and formal se- 
mantics are unequal to the task of selecting one of the 
many possible parses and interpretations of a given text, 
but human readers easily choose just one interpretation. 
NS representations are very useful in guiding this 
choice. They can be used to disambiguate parse trees, 
word senses, and quantifier scope. We use an existing 
parser (MODL) to get one of the syntactic structures for 
a sentence and modify it based upon NS information. 
This allows us to isolate the power of NS representa- 
tions with respect to the parsing problem. Not only are 
NS representations necessary for a robust parsing ca- 
pability, but also in anaphora resolution and discourse 
reasoning. Furthermore, the parse tree must be avail- 
able to the discourse coherence rules. Thus our re- 
search has shown that not only must the NS represen- 
tations be accessible at all levels of text processing, but 
purely syntactic, semantic, and pragmatic information 
that has been accumulated must also be available to 
later stages of processing. As a result, the architecture 
of the system involves separate modules for syntax, 
semantics, discourse and naive semantics, but each of 
the modules has access to the output of all others, as 
shown in Figure I. 
The parser chosen is the Modular Logic Grammar 
(McCord 1987), (MODL). Both MODL and KT are 
written in VM/PROLOG (IBM 1985). The input to 
MODL is a sentence or group of sentences (a text). In 
KT we intercept the output of MODL at the stage of a 
labeled bracketing marked with grammatical features 
and before any disambiguation or semantic processing 
is done. In effect, we bypass the semantics of MODL in 
Computational Linguistics, Volume 15, Number 3, September 1989 155 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
order to test our NS representations. In the labeled 
bracketing each lexical item is associated with an argu- 
ment structure that can be exploited in semantic inter- 
pretation. The labeled bracketing output by MODL is 
slightly processed before being passed to our module 
DISAMBIG. Here the commonsense knowledge base is 
accessed to apply rules for prepositional phrase attach- 
ment (Dahlgren and McDowell 1986b) and word sense 
disambiguation (Dahlgren 1988a), as well as to assign 
the correct scope properties to operators and quantifi- 
ers. The output is a modified parse. All of these modules 
are in place and functional. The word sense disambig- 
uation rules are in the process of being converted. An 
example of the input to DISAMBIG and the resulting 
output is as follows: 
11. Input S: 
John put the money in the bank. 
Input to DISAMBIG: 
s(np(n(n(john(Vl)))) & 
vp(v(fin(pers3,sg,past,ind),put(V I,V2)) 
np(detp(the(V3,V4)) & n(n(money(V2))) 
pp(p(in(V2,VS)) & np(detp(the(V6,VT)) 
n(n(bank(VS)))))))) 
Output of DISAMBIG: 
s(np(n(n(John(Vl)))) 
vp (v(fln(pers3,sg,past~d),put i (V I,V2 )) 
np(detp(the(V3,V4)) & n(n(money(V2)))) & 
pp(p(in(V2,VS)) & np(detp(the(V6,VT)) & 
n(n(bank2(VS))))))) 
The differences are that the PP "in the bank" is 
VP-attached in the output of DISAMBIG rather than 
NP-attached as in the output of MODL, and that the 
words "put" and "bank" are assigned index numbers 
and changed to putl and bank2, selecting the senses 
indicated by the word sense disambiguation algorithm. 
2.2.2 SENTENCE-LEVEL SEMANTICS 
The modified parse is then submitted to a semantics 
module, which outputs a structure motivated in part by 
current versions of discourse representation theory 
(DRT) (Kamp 1981; Wada and Asher 1986; Asher and 
Wada 1988). The actual form of the discourse represen- 
tation structure (DRS) and its conditions list in the KT 
system differ from standard formats in DRT in that 
tense arguments have been added to every predicate 
and tense predicates link the tense arguments to the 
tense of the verb of the containing clause. The analysis 
of questions and negation was carried out entirely with 
respect to the KT system and to serve its needs. The 
DRT semantics is in place and functional for most 
structures covered by MODL. The commonsense 
knowledge representations are accessed in the DRT 
module for semantic interpretation of modals, the de- 
termination of sentence-internal pronoun anaphora 
(where simple C-command and agreement tests fail), 
and to determine some cases of quantifier scoping. 
2.2.3 DISCOURSE-LEVEL SEMANTICS 
As each sentence of a text is processed it is added to the 
DRS built for the previous sentence or sentences. Thus 
an augmented DRS is built for the entire text. In the 
augmentation module the commonsense knowledge rep- 
resentations are accessed to determine definite noun 
phrase anaphora, sentence-external pronoun anaphora, 
temporal relations between the tense predicates gener- 
ated during sentence-level DRS construction, discourse 
relations (suc\]h as causal relations) between clauses, and 
the rhetorical structure of the discourse as a whole. The 
discourse work is being carried out mainly by Dahlgren 
and is in various stages of completion. 
2.2.4 FOL 
Since standard proof techniques are available for use 
with logical forms, the DRS formulated by the sentence- 
level and discourse-level semantic components is con- 
verted to standard logic. A number of difficulties 
present themselves here. In the first place, given any of 
the proposed semantics for DRSs (e.g., Kamp 1981; 
Asher 1987), DRSs are not truth functional. That is, the 
truth walue of a DRS structure (in the actual world) is 
not generally a function of the truth values of its 
constituents. For example, this happens when verbs 
produce opaque contexts (Asher 1987). Since general 
proof methods for modal logics are computationally 
difficult, we have adopted the policy of mapping DRSs 
to naiw ~ , first-order translations in two steps, providing 
a special and incomplete treatment of non-truth func- 
tional contexts. The first step produces representations 
that differ minimally from standard sentences of first- 
order logic. The availability of this level of representa- 
tion enhances the modularity and the extensibility of the 
system. Since first-order reasoning is not feasible in an 
application like this, a second step converts the logical 
forms to clausal forms appropriate for the problem 
solver or the textual knowledge base. We describe each 
of these steps in turn. 
2.2.4.1 THE TRANSLATION TO STANDARD LOGICAL FORMS 
The sentence-level and discourse-level semantic com- 
ponents disambiguate names and definite NPs, so that 
each discourse reference marker is the unique canonical 
name for an individual or object mentioned in the 
discourse. The scoping of quantifiers and negations has 
also been determined by the semantic processing. This 
allows the transformation of a DRS to FOL to be rather 
simple. The basic ideas can be briefly introduced here; 
a more thorough discussion of the special treatment of 
queries is provided below. The conditions of a DRS are 
conjoined. Those conditions may include some that are 
already related by logical operators (if-then, or, not), in 
which case the logical form includes the same opera- 
tors. Discourse referents introduced in the consequent 
of an if-then construction may introduce r quantifiers: 
these are given narrow scope relative to quantifiers in 
the consequent (cf. Kamp's notion of a "subordinate" 
156 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
DRS in Kamp 1981; Asher and Wada 1988). Any 
quantifiers needing wide scope will already have been 
moved out of the consequent by earlier semantic proc- 
essing. The DRSs of questions contain special operators 
that, like the logical operators, take DRS arguments that 
represent the scope of the material being questioned. A 
similar indication is needed in the logical form. In the 
case of a yes-no question, we introduce a special 
vacuous quantifier just to mark the scope of the ques- 
tioned material for special treatment by the problem 
solver (see below). In the case of wh-questions, a 
special wh-quantifier is introduced, again indicating the 
scope of the questioned material and triggering a special 
treatment by the problem solver. Verbs of propositional 
attitude and other structures with opaque contexts are 
treated as, in effect, introducing new isolated subtheo- 
des or "worlds." For example, "John believes all men 
are mortal" is represented as a relation of belief holding 
between John and a logical form (see below for more 
detail), though it is recognized that this approach will 
need to be supplemented to handle anaphora and prop- 
ositional nominals (cf., e.g., Asher 1988). 
2.2.4.2 THE CONVERSION TO SPECIALIZED 
CLAUSAL FORMS 
Considerations of efficiency motivate a further transfor- 
mation in our logical forms. After the first step of 
processing, we have standard first-order logical forms, 
except that they may include special quantifiers indicat- 
ing questioned material. Consider first those logical 
forms that are not inside the scope of any question 
quantifier. These are taken as representations of poten- 
tial new knowledge for the textual data base. Since the 
inference system must solve problems without user 
guidance, it would be infeasible to reason directly from 
the first-order formulations. Clausal forms provide an 
enormous computational advantage. For these reasons, 
we transform each sentence of first-order logic into a 
clausal form with a standard technique (cf., e.g., Chang 
and Lee 1973), introducing appropriate Skolem func- 
tions to replace existentially quantified variables. The 
textual database can then be accessed by a clausal 
theorem prover. In the current system, we use efficient 
Horn clause resolution techniques (see below), so the 
knowledge representation is further restricted to Horn 
clauses, since completeness is less important than fea- 
sible resource use in the present application. Definite 
clauses are represented in a standard Prolog format, 
while negative clauses are transformed into definite 
clauses by the addition of a special positive literal 
"false(n)," where n is an integer that occurs in no other 
literal with this predicate. This allows a specialized 
incomplete treatment of negation-as-inconsistency (cf. 
Gabbay and Sergot 1986). The definite clause transla- 
tions of the text can then be inserted into a textual 
knowledge base for use by the reasoning component. 
The presence of question quantifiers triggers a special 
treatment in the conversion to clausal form. Our prob- 
lem solver uses standard resolution techniques: to 
prove a proposition, we show that its negation is 
incompatible with the theory. Accordingly, the material 
inside the scope of a question operator is treated as if it 
were negated, and this implies an appropriately differ- 
ent treatment of any quantifiers inside the scope of the 
operators. 
2.2.5 REASONER 
In the architecture of the system, the reasoning module 
is broken into two parts: the specialized query process- 
ing system and a general purpose problem solver. The 
special processing of queries is described in detail 
below. The problem solver is based on a straightforward 
depth-bounded Horn clause proof system, implemented 
by a Prolog metainterpreter (e.g. Sterling and Shapiro 
1986). The depth bound can be kept fixed when it is 
known that no proofs should exceed a certain small 
depth. When a small depth bound is not known, the 
depth can be allowed to increase iteratively (cf. Stickel 
1986), yielding a complete SLD resolution system. This 
proof system is augmented with negation-as-failure for 
predicates known to be complete (see the discussion of 
open and closed world assumptions below), and with a 
specialized incomplete negation-as-inconsistency that 
allows some negative answers to queries in cases where 
negation-as-failure cannot be used. 
2.2.6 RELEVANCE 
The RELEVANCE module will have the responsibility 
of determining the relevance of a particular text to a 
particular user. The text and user profiles will be 
processed through the system in the usual way resulting 
in two textual data bases, one for the target text and one 
for the profile. Target and profile will then be compared 
for relevance and a decision made whether to dispatch 
the target to the profiled user or not. The relevance 
rules are a current research topic. The commonsense 
knowledge representations will form the primary basis 
for determining relevance. 
3 NAIVE SEMANTICS IN THE KT SYSTEM 
From the foregoing brief overview of the KT system, it 
should be clear that naive semantics is used throughout 
the system for a number of different processing tasks. In 
this section we show why each of these tasks is a 
problem area and how NS can be used to solve it. 
3.1 PREPOSITIONAL PHRASE ATTACHMENT 5 
The proper attachment of post-verbal adjuncts is a 
notoriously difficult task. The problem for prepositional 
phrases can be illustrated by comparing the following 
sentences. 
12. \[S The government \[VP had uncovered \[NP an 
entire file \[PP about the scheme\]\]\]\]. 6 
13. \[S Levine's lawyer \[VP announced \[NP the plea 
bargain\] \[PP in a press conference\] 
Computational Linguistics, Volume 15, Number 3, September 1989 157 
Kathleen Dahigren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
14. \[S\[S The judge adjourned the hearing\] \[PP in the 
afternoon\]\] 
Each of these sentences takes the canonical form Sub- 
ject-Verb-Object-PP. The task for the processing sys- 
tem is to determine whether the PP modifies the object 
(i.e., the PP is a constituent of the NP, as in (12)), the 
verb (i.e., the PP is a constituent of the VP, as in (13)), 
or the sentence (i.e., the PP is an adjunct to S, as in 
(14)). Some deny the need for a distinction between VP 
and S-modification. The difference is that with S-mod- 
ification, the predication expressed by the PP has scope 
over the subject, while in VP-attachment it does not. 
For example, in (15), "in the park" applies to Levine, 
while in (16), "with 1,000 dollars" does not apply to 
Levine. 
15. Levine made the announcement in the park. 
16. Levine bought the stock with 1,000 dollars. 
A number of solutions for the problem presented by 
post-verbal prepositional phrases have been offered. 
The most common techniques depend on structural 
(Frazier and Fodor 1978), semantic (Ford, Bresnan, and 
Kaplan 1982), or pragmatic (Crain and Steedman 1985) 
tests. MODL (McCord 1987) employs a combination of 
syntactic and semantic information for PP attachment. 
Independently, we formulated a preference strategy for 
PP attachment which uses ontological, generic and 
syntactic information to cover 99% of the cases in an 
initial test corpus, and which is 93% reliable across a 
number of types of text. This is the preference strategy 
we employ in KT. The PP attachment rules make use of 
information about the verb, the object of the verb, and 
the object of the preposition. A set of global rules is 
applied first, and if these fail to find the correct attach- 
ment for the PP, a set of rules specific to the preposition 
are tried. Each of these latter rules has a default. The 
global rules are stated informally in (17). with example 
sentences. 
17a. time(POBJ)-, s_attach(PP) 
If the object of the preposition is an expression 
of time, then S-attach the PP. 
The judge adjourned the hearing in the after- 
noon. 
b. lexical(V+Prep)--~ vp_attach(PP) 
If the verb and preposition form a lexicalized 
complex verb, then VP-attach the PP. 
The defense depended on expert witnesses. 
c. Prep=of--~ np_attach(PP) 
If the preposition is of then NP-attach the PP. 
The ambulance carried the victim of the shoot- 
ing. 
d. intransitive(V) & motion(V) & place(POBJ) 
--~ vp_attach(PP) 
If the verb is an intransitive verb of motion and 
the object of the preposition is a place then 
VP-attach the PP. 
The press scurried about the courtroom. 
e. 2intransitive(V) & (place(POBJ) OR temporal 
(POBJ) OR abstract(POBJ)) --* a.attach(PP) 
If the verb is intransitive and the object of the 
preposition is a place, temporal, or abstract, 
then S-attach the PP. 
Levine worked in a brokerage house. 
f. epistemic(POBJ)--* s_attach(PP) 
If the prepositional phrase expresses a proposi- 
tional attitude, then attach the PP to the S. 
Levine was guilty in my opinion. 
g. xp(.. ~_dj-PP...)--~ xp_attach(PP) 
If PP follows an adjective, then attach the PP to 
the phrase which dominates and contains the 
adjective phrase. 
Levine is young for a millionaire. 
h. measure(DO)--~ np_attach(PP) 
If the direct object is an expression of measure, 
then NP-attach the PP. 
The defendant had consumed several ounces of 
whiskey. 
i. comparative-* np_attach(PP) 
If there is a comparative construction, then 
NP-attach the PP. 
The judge meted out a shorter sentence than 
usual. 
j. mental(V) & medium(POBJ)-~ vp_attach(PP) 
If the verb is a verb of saying, and the object of 
the preposition is a medium of expression then 
VP-attach the PP. 
Levine's lawyer announced the plea bargain on 
television. 
Example 14 is handled by global rule (17a). Example 13 
is handled by global rule (17j). The global rules are 
inapplicable with example 12, so the rules specific to 
"about" are called. These are shown below. 
18a. intrsmsitive(V) & mental(V)--~ vp_attach(PP) 
If the verb is an intransitive mental verb, then 
VP-attach the PP. 
Levine spoke about his feelings. 
b. Elsewhere--* np_attach(PP) 
Otherwise, NP-attach the PP. 
The government had uncovered an entire file 
about the scheme. 
As a filrther example, the specific rules for "by" uses 
both generic (19a,b) and ontological (19c) knowledge. 
19 a. nom(DO)--~ np_attach(PP) 
If the direct object is marked as a nominaliza- 
tion, then NP-attach the PP. 
The soldiers withstood the attack by the en- 
emy. 
b. location(DO,POBJ)--~ np_attach(PP) 
If tJ~e rel~.tion between the d.ireot obJeot and 
t~he object of t~he preposition Is one of loos,- 
~ion, then lq'P-~t~ch t, he PP. 
The clerk adjusted the microphone by the 
witness stand. 
158 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commortsense Reasoning with Text 
c. proposltional(DO) & sentlent(POBJ)--, np_at- 
ta~h(PP) 
If the direct object is propositional and the 
object of the preposition is sentient, then NP- 
attach the PP. 
The judge read out the statement by Levine. 
d. Elsewhere---> s_attach(PP) 
Otherwise, S-attach the PP. 
The lawyers discussed the case by the parking 
lot. 
These PP-attachment preference rules are remarkably 
successful when applied to real examples from actual 
text. However, they are not foolproof and it is possible 
to construct counterexamples. Take the global rule 
illustrated in (17a). We can construct a counterexample 
as in (20). 
20. John described the meeting on Jan. 20th. 
Sentence 20 is ambiguous. Jan. 20th can be the time of 
the describing or the time of the meeting. Perhaps there 
is a slight intuitive bias toward the latter interpretation, 
but the rules will assign the former interpretation. This 
is a counterexample only because "meeting" is a TEM- 
PORAL noun and can plausibly have a time feature. 
Compare (21), which is identical except for the ontolog- 
ical attachment of the direct object and which is handled 
correctly by the global rule. 
21. John described the proposal on Jan. 20th. 
The problem of the interpretation of event nominals is a 
research topic we are working on. 
The PP attachment rules are applied in the module 
DISAMBIG, which produces a disambiguated parse 
from the output of the MODL parser. The first step is to 
identify the sentence elements that form their inputs for 
each clause using find..args. The output of find_args is a 
list of the the direct object, object of the preposition, 
preposition, and main verb of the clause and the index 
of the clause (main, subordinate, and so on). The 
PP-attachment rules are in place and functional for one 
post-verbal prepositional phrase. Where more than one 
post-verbal prepositional phrase occurs, the current 
default is to attach the second PP to the NP of the first 
PP. However, this will not get the correct attachment in 
cases like the following. 
22. The judge passed sentence on the defendant in a 
terse announcement. 
A planned extension of the PP-attachment functionality 
will attack this problem by also keeping a stack of 
prepositions. The top of the stack will be the head of the 
rightmost PP. The attachment rules will be applied to 
PPs and the other constituents in a pairwise fashion 
until all are attached. 
3.2 WORD SENSE DISAMBIGUATION 
The word sense disambiguation method used in the 
system is a combined local ambiguity reduction method 
(Dahlgren 1988b). The method is local because word 
senses are disambiguated cyclically, from the lowest 
S-node up to the matrix node. Only when intrasentential 
sources of information fail are other sentences in the 
text considered by the disambiguation method. The 
algorithm is combined because it employs three sources 
of information. First it tries fixed and frequent phrases, 
then word-specific syntactic tests, and finally naive 
semantic relationships in the clause. If the fixed and 
frequent phrases fail, the syntactic and naive semantic 
rules progressively reduce the number of senses rele- 
vant in the clausal context. The algorithm was devel- 
oped by considering concordances of seven nouns with 
a total of 2,193 tokens of the nouns, and concordances 
of four verbs with a total of 1,789 tokens of the verbs. 
The algorithm is 96% accurate for the nouns in these 
concordances, and 99% accurate for the verbs in these 
concordances. 
Fixed phrases are lists of phrases that decisively 
disambiguate the word senses in them. For example, the 
noun "hand" has 16 senses. Phrases such as "by 
hand," "on hand," "on the one hand" have only one 
sense. 
Syntactic tests either reduce the number of relevant 
senses, or fully disambiguate. For nouns, syntactic tests 
look for presence or absence of the determiner, the type 
of determiner, certain prepositional phrase modifiers, 
quantifiers and number, and noun complements. For 
example, only five of the 16 senses of "hand" are 
possible in bare plural noun phrases. For verbs, syntac- 
tic tests include the presence of a reflexive object, 
elements of the specifier, such as particular adverbs the 
presence of a complement of the verb and particular 
prepositions. For example, the verb "charge" has only 
its reading meaning "indict" when there is a VP- 
attached PP where the preposition is "with" (as deter- 
mined by the prepositional phrase attachment rules). 
Syntactic tests are encoded for each sense of each 
word. The remainder of this section will illustrate 
disambiguation using naive semantic information and 
give examples of the naive semantic rules. (The com- 
plete algorithm may be found in Dahlgren 1988a). 
3.2.1 NOUN DISAMBIGUATION 
Naive semantic information was required for at least a 
portion of the disambiguation in 49% of the cases of 
nouns in the concordance test. Naive semantic infer- 
ence involves either ontological similarity or generic 
relationships. Ontological similarity means that two 
nouns are relatively close to each other in the ontology, 
both upwards and across the ontology. If there is no 
ontological similarity, generic information is inspected. 
Generic information for the ambiguous noun, other 
nouns in the clause, the main verb, often disambiguate 
an ambiguous noun. 
Ontological similarity is tested for in several syntac- 
tic constructions: conjunction, nominal compounds, 
possessives, and prepositional phrase modifiers. Many 
Computational Linguistics, Volume 15, Number 3, September 1989 159 
Kathleen Dahlgren, Joyee McDowell, and Edward P. Stabler, Jr. Knowiedge Representation for Commonsense Reasoning with Text 
of the 16 senses of "hand" are ontologically distinct, as 
shown in Table 7. 
I. HUMAN human body part 
2. DIRECTION right or left 
3. INSTRUMENT by hand 
4. SOCIAL power, authority 
5. TEMPORAL applause 
6. ROLE laborer 
7. ARTIFACT part of a clock 
Table 7. Some senses of hand 
In (23), only the HUMAN and ROLE senses (I and 
6) are possible, by ontological similarity. Generic 
knowledge of the verb "clear" is inspected for the final 
disambiguation to sense 6. 
23. The farmer and his hand cleared the field. 
In contrast, in (24), the relevant senses of "hand" are 
the HUMAN and ARTIFACT senses (1 and 7). 
24. His arm and hand were broken. 
At the point in the algorithm where naive semantic tests 
are invoked, syntactic tests have already eliminated the 
ARTIFACT sense, which does not occur with a per- 
sonal pronoun. Thus the HUMAN sense is selected. In 
(25), only the ARTIFACT sense (7) is possible, by 
ontological similarity of "clock" and "hand." 
25. The clock hand was black. 
In (26), again the HUMAN and ROLE senses (1 and 6) 
are the only relevant ones by ontological similarity. 
Selection restrictions on "shake" complete the disam- 
biguation. 
26. John shook the man's hand. 
In (27), sense 4 is selected because "affair" and sense 4 
are both SOCIAL. 
27. John saw his hand in the affair. 
In (28), sense I is selected because both sense 1 and a 
sense of "paper" are attached to PHYSICAL. 
28. The judge had the paper in his hand. 
The word sense disambiguation algorithm tests for 
generic relationships between the ambiguous noun and 
prepositional phrases modifiers, adjective modifiers, 
and the main verb of the sentence. Two of the nine 
senses of "court" are shown in Table 8. In "the court 
listened to testimony," generic information for the 
second sense of "court" can be used to select sense 2. 
The generic information includes knowledge that one of 
the functions of courts has to do with testimony. In (29), 
sense 1 of "court" is selected because the generic 
representation of "court" contains information that 
witness stands are typical parts of courtrooms. 
courtl 
court2 
PLACE Typically, it has a bench, 
jury box, and witness stand. 
Inherently its function is for 
a judge to conduct trials in. 
It is part of a courthouse. 
INSTITUTION Typically, its function is 
justice. Examples are the 
Supreme Court and the 
superior court. Its location 
is a courtroom. Inherently it 
is headed by a judge, has 
bailiffs, attorneys, court 
reporters as officers. 
Participants are defendants, 
witnesses and jurors. The 
function of a court is to 
hear testimony, examine 
evidence and reach a 
verdict. It is part of the 
justice system. 
Table 8. Generic Information for Two Senses of court 7 
29. The witness stand in Jones's court is made of oak. 
In (30), the adjective "wise" narrows the relevant 
senses from nine to the two INSTITUTION senses of 
"court." 
30. 'The wise court found him guilty. 
Generic knowledge of one sense of the verb "find" is 
then used to select between the court-of-law sense (2) 
and the-royal-court sense (4). Verb selection restric- 
tions are powerful disambiguators of nouns, as many 
computational linguists have observed. In (31), the verb 
chargel 
charge2 
charge3 
Table 9. 
Typically, if someone is charged, next they 
are indicted in court, convicted or acquitted. 
They are charged because they have 
committed a crime or the person who 
charges them suspects they have. 
Inherently, the charger and chargee are 
sentient, and the thing charged with is a 
crime. 
Inherently, if someone charges that 
something is true, that someone is sentient, 
his goal is that it be known, and the 
something is bad. 
Typically, if someone charges someone else 
an amount for something, the chargee has to 
pay the amount to the charger, and the 
chargee is providing goods or services. 
Inherently, the charger and chargee are 
sentients, the amount is a quantity of 
money, and the goal of the charger is to 
have the chargee pay the amount. 
Generic Information for Thi'ee Senses of charge 
160 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDoweli, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
"last" requires a TEMPORAL subject, thus disambig- 
uating "hand." 
31. They gave him a hand which lasted 10 minutes. 
3.2.2 VERB DISAMBIGUATION 
Just as selectional restrictions of verbs disambiguate 
nouns, their arguments are powerful disambiguators of 
verbs. Subject, object, and oblique arguments are all 
taken into account by the algorithm. (32) and (33) 
illustrate the way that objects can disambiguate the verb 
"charge," generic entries for which are shown in Table 
9. 
In (32) the SENTIENT object selects sense 1. In (33), 
the MONEY object selects sense 3. 
32. The state charged the man with a felony. 
33. The state charged ten dollars for the fine. 
The verb "present" can be disambiguated by its sub- 
ject. It has at least three senses: 
34. present l--"give" 
present2---"introduce" 
present3--"arrive in the mind of" 
Senses 1 and 2 require SENTIENT subjects, so the 
third sense is selected in (35). 
35. The decision presented a problem to banks. 
(36) illustrates subject and object disambiguation. The 
SENTIENT subject narrows the possibilities to senses 
l and 2, and the "give" sense (1) is selected because it 
requires a PHYSICAL object argument. 
36. John presented a bouquet to Mary. 
3.2.3 OISAMBIGUATION RULES 
The disambiguation method first tries fixed and frequent 
phrases, then syntactic tests, and finally naive semantic 
information. Each set of rules reduces the number of 
relevant senses of a word in the sentential (and extra- 
sentential) context. There is a fixed set of commonsense 
rules for nouns and another one for verbs. They are 
tried in an order that inspects the main verb of the 
clause last, because the main verb often chooses be- 
tween the last two relevant senses. An example of a 
noun rule is ppmod, which considers an ambiguous 
noun in relation to the head of a prepositional phrase 
modifier attached to the same higher NP as the ambig- 
uous noun. There are two versions of the rule, one 
which looks for ontological similarity between senses of 
the ambiguous noun and the head of the PP, and one 
which looks for generic relationships between them. 
The output of find_args (in DISAMBIG, see Section 3. l) 
is used as a simplified syntactic structure inspected by 
these rules. This provides information as to whether or 
not a head noun is modified by a prepositional phrase. 
In the first version of the rule, the ontological attach- 
ment of the head of the PP is looke d up and then senses 
of the ambiguous word with that same ontological 
attachment are selected. SI is the list of senses of the 
ambiguous noun still relevant when the rule is invoked. 
$2 is the list of senses reduced by the rule if it succeeds. 
If the first version fails, the second is invoked. It looks 
for a generic relationship between senses of the ambig- 
uous word and the head of the PP. 
37. ppmod(Ambig_Word,{.. _A_rnbig_Word,Prep, 
Noun...},Sl,S2) ~- 
ontattach(Noun, Node) & 
ontselect (Ambig_Word,Node, Sl,S2). 
ppmod(Ambig_Word,{.. _&mbig_Word,Prep, 
Noun...},Sl,S2) ~-- 
generic_relation( Nound%rnbig_Word ). 
3.3 QUANTIFIER SCOPING 
The semantic module of the KT system is capable of 
generating alternative readings in cases of quantifier 
scope ambiguities, as in the classic case illustrated in 
(38). 
38. Every man loves a woman. 
In this example either the universally quantified NP 
("every man") or the existentially quantified NP ("a 
woman") can take widest scope. In such sentences, it is 
generally assumed that the natural scope reading (left- 
most quantified expression taking widest scope) is to be 
preferred and the alternative reading chosen only under 
explicit instructions of some sort (such as input from a 
user, for example, or upon failure to find a proper 
antecedent for an anaphoric expression). Under this 
assumption, in a sentence like (39), the indefinite NP 
would preferentially take widest scope. 
39. A woman loves every man. 
But there are a number of cases similar to (39) where an 
expression quantified by "every" appears to the right of 
an indefinite NP and still seems to take widest scope. 
Ioup (1975) has discussed this phenomena, suggesting 
that expressions such as "every" take widest scope 
inherently. Another computational approach to this 
problem is to assign precedence numbers to quantifiers, 
as described in McCord (1987). However, our investi- 
gation has shown that commonsense knowledge plays at 
least as large a role as any inherent scope properties of 
universal quantifiers. 
Consider (40). In the natural scope reading, "every 
lawyer" takes scope over "a letter" and we have 
several letters, one from each of the lawyers, i.e., 
several tokens of a one-to-one relationship. In the 
alternative reading, "a letter" takes scope over "every 
lawyer" and we have only one letter, i.e., one token of 
a many-to-one relationship relationship. Both scope 
readings are plausible for (40). 
40. The judge read a letter from every lawyer. 
In (41) only the alternative reading (several tokens of 
one-to-one) is plausible. 
41. The politician promised a chicken in every pot. 
In (42), however, only the natural reading (one token of 
many-to-one) is plausible. 
42. The prince sought a wife with every charm. 
Even in (40), however, speakers prefer the one-to-one 
relationship, the alternative reading. That is, speakers 
prefer the reading that denotes several tokens of a 
one-to-one relationship (several letters) over one which 
denotes one token of a many-to-one relationship unless 
Computational Linguistics, Volume 15, Number 3, September 1989 161 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
there is strong commonsense knowledge to override this 
preference. We know that in our culture princes can 
h~ive only one wife, so in the case of (42) speakers prefer 
one token (one wife) of many-to-one to several tokens 
of one-to-one. Similar arguments apply to the following 
examples (43)-(45), which correspond to (40)-(42) re- 
spectively. 
43. A judge decided on every petition. 
44. A lawyer arrived from every firm. 
45. A company agent negotiated with every union. 
Thus, if there is an inherent tendency for universal 
quantifiers to take widest scope where scope ambiguity 
is possible, it derives from the human preference for 
viewing situations involving universally quantified enti- 
ties as many tokens of one-to-one relationships, s In KT, 
first we prefer wide scope for the universal quantifier. 
In cases where this is not the natural scope interpreta- 
tion, (i.e., the universal quantifier is to the right of a 
containing existentially quantified NP), we can use facts 
encoded in the generic data base to override the pref- 
erence. For example, when processing (42) we would 
discover that a man may have only one wife. The 
generic entry for "wife" tells us that "wife" is a role in 
a "marriage." The generic entry for "marriage" tells us 
that "marriage" is a relation between exactly one 
husband and exactly one wife. This knowledge forces 
the many-to-one interpretation. The cases where this is 
necessary turn out to be rare. Curiously, the preposition 
"with" correlates very highly with many-to-one rela- 
tionships. Thus our strategy for the present has been to 
consider overriding the preference only when the uni- 
versal quantifier is in an NP which is the object of 
"with." In these cases we access the generic knowl- 
edge, as described above. 
3.4 OPAQUE CONTEXTS 
In KT, clauses in opaque contexts (embedded under 
propositional attitude verbs such as "hope," "be- 
lieve," "deny") are handled by asserting the predicates 
generated from the clause into partitioned databases, 
which correspond to the delineated DRSs of Asher 
(1987). Each partition is associated with the speaker or 
the individual responsible for the embedded clause. 
Reasoning can then proceed taking into account the 
reliability and bias of the originator of the partitioned 
statements, as in Section 2.2.4.1 An example follows. 
46. Text: Meese believes that Levine is guilty. 
Textual Database: believe(sl,meese,pl) 
Partition: p 1 :: guilty(s2,1evine) 
3.5 MODALS 9 
The English modals can, may, must, will, and should 
are high-frequency items in all kinds of texts. They can 
be easily parsed by a single rule similar to the rules that 
handle auxiliary "have" and "be" because all the 
modals occupy the same surface syntactic position (i.e., 
the first element in the auxiliary sequence). However, 
the~ modals present some considerable problems for 
semantic interpretation because they introduce ambigu- 
ities and induce intensional contexts in which possibil- 
ity, necessity, belief, and value systems play a role. In 
the KT system, we are concerned with what is known 
by the system as a result of reading in a modal sentence. 
In particular we are interested in what status the system 
assigns to the propositional content of such a sentence. 
To illustrate the problem, if the system reads 
"Levine engaged in insider trading," then an assertion 
can justifiably be added to the knowledge base reflect- 
ing the fact that Levine engaged in insider trading. The 
same is true if the system reads "The Justice Depart- 
ment knows that Levine engaged in insider trading." 
But this is not the case if the system reads "The Justice 
Department believes that Levine engaged in insider 
trading." In this case the statement that Levine engaged 
in insider trading must be assigned some status other 
than fact. Specifically, since "believe" introduces an 
opaque context, the propositional content of the embed- 
ded clause would be assigned to a partitioned data base 
linked to the speaker the Justice Department, as de- 
scribed in the previous section. A similar problem exists 
in modal sentences such as "Levine may have engaged 
in insider trading." 
There are two types of modal sentences. In Type I 
modal sentences the truth value of the propositional 
content is evaluated with respect to the actual world or 
a set of possible other states of the actual world directly 
inferable from the actual world. Examples are 
47. Levine must have engaged in insider trading. 
48. The Justice Department will prosecute Levine. 
49. Levine can plead innocent. 
In Type II modal sentences, we say that a second 
speech act is "semantically embedded" in the modal 
sentence. The modal sentence is successful as an asser- 
tion just in case the secondary speech act is in effect in 
the actual world. In these cases the truth value of the 
propositional content is evaluated with respect to some 
set of deontic or normative worlds. The modal is viewed 
as a quantifier cum selection function. Thus, for a 
sentence of the form/~ = NP Modal VP, I~ is true in the 
actual world just in case NP VP is true in at least 
one/every world (depending on Modal) in the set of 
deontic or normative worlds selected by Modal. Exam- 
ples are 
50. Levine must confess his guilt. 
51. Levine may make one phone call. 
52. Levine should get a good attorney. 
In (50) a command is semantically embedded in the 
assertion; in (51) a permission is semantically embedded 
in the assertion; and in (51) the issuance of a norm is 
semantically embedded in the assertion. 
Type I modal sentences are of assertive type accord- 
ing to the speech act classification scheme in Searle and 
Vanderveken (1985). These include the standard asser- 
tions, reports, and predictions, and a proposed new 
162 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
type, quasi-assertion. They must be distinguished in a 
text-understanding system from Type II modal sen- 
tences that embed other types of speech acts, because 
only Type I modal sentences make a contribution to the 
textual knowledge base. In addition, some modal sen- 
tences are ambiguous between Type I and Type II, for 
example (47), (51), and (50). 
For the KT system, disambiguating the ambiguous 
modals "may" and "must" results in changing the 
syntactic input to the semantic module. The surface 
syntactic parse that is output by the parser is converted 
into the equivalent logical form where the epistemic 
(Type I) uses of ambiguous modals are treated as 
sentential operators and the nonepistemic uses of am- 
biguous modals (Type II) are treated as modifiers of the 
verb. Sentences containing ambiguous modals can be 
assigned the correct status by a simple disambiguation 
algorithm that makes appeal to the ontological classifi- 
cation of the main verb, specifically whether or not the 
verb is STATIVE, following Steedman (1977). Disam- 
biguation takes place in DISAMBIG, the same module 
that converts the labelled bracketing to a modified parse 
for input to the DRT semantic translator. At this point, 
a determination is made whether the modal is a senten- 
tial operator or a modifier of the verb. The propositional 
content of quasi-assertions and predictions can be 
added directly to the dynamically constructed textual 
data base if they are appropriately marked with proba- 
bility ratings. On this view, "will" and one sense of 
"may" are taken as denoting strong and weak predic- 
tion and are not viewed as tenses. That is, when using 
these modals, the speaker is indicating his confidence 
that there is a high/moderate probability of the propo- 
sitional content being true in the future. In the present 
state of the system, every predicate contains a tense 
argument and there is a tense predicate relating every 
tense argument to a tense value (such as "pres.," 
"future," etc.).1° In a planned extension to the system 
these tense predicates will also contain probability 
ratings. For example, given the continuum of speaker 
commitment to the truth of the statement "Leyine 
engaged in insider trading" illustrated in (53), we would 
have the corresponding predicates in the DRS shown in 
(54). 
53. Full Assertion: Levine engaged in insider trading 
Strong Quasi-Assertion: Levine must have en- 
gaged in insider trading 
Weak Quasi-Assertion: Levine may have en- 
gaged in insider trading 
54. Full Assertion: engage(el,levine), tense(el ,past, 1) 
Strong Quasi-Assertion: engage(el,levine), tense 
(el,past,0.9) 
Weak Quasi-Assertion: engage(el,levine), tense 
(el,past,0.5) 
This hierarchy reflects the "epistemic paradox" of 
Karttunen (1971), in which he points out that in stan- 
dard modal logic must(P) or necessarily, P is stronger 
than plain assertion whereas epistemically-must(P) is 
weaker than plain assertion. This results from the fact 
that the standard logic necessity operator quantifies 
over every logically possible world, plain assertion of P 
is evaluated with respect to the actual world, but the 
epistemic modal operator quantifies only over the 
epistemically accessible worlds, a set which could pos- 
sibly be null. 
Assertions of possibility (49) t¢igger the inferring of 
enabling conditions. For any event there is a set of 
enabling conditions that must be met before the event is 
possible. For John to play the piano, the following 
conditions must be met: 
55. 1. John knows how to play the piano. 
2. John has the requisite permissions (if any). 
3. A piano is physically available to John. 
4. John is well enough to play. 
.... 
These can be ordered according to saliency as above. 
This, we claim, is why the sentence "John can play the 
piano" most often receives the interpretation (I), less 
often (2), and practically never (3) or (4) unless explic- 
itly stated, as in "John can play the piano now that his 
mother has bought a new Steinway." The enabling 
conditions are encoded in KT as part of the generic 
representation for verbs. When a modal sentence is 
interpreted as a full assertion of possibility (poss(p)), 
this triggers the inference that the most salient of the 
enabling conditions is in fact true. The difference be- 
tween poss(p) and p being processed for KT, is that ifp 
is output, then p is added to the textual database and the 
most salient enabling condition is also inferred. But if 
poss(p) is output, then only the most salient enabling 
condition is inferred, but p is not added to the textual 
database. Notice that this simply reflects the fact that if 
I say "John can play the piano," I am not saying that 
John is playing the piano at that very moment. 
Type II modal sentences present a more complex 
problem for interpretation. The commands, permis- 
sions, and norms reported in Type II modal sentences 
are asserted into partitioned databases in the same way 
as clauses in opaque contexts. The only difference is 
that in most cases the issuer of the command, permis- 
sion, or norm reported in a modal sentence is not 
known. Semantic translation in DRT proceeds via cat- 
egorial combination. By the time the modal sentence 
reaches the DRT module, the semantic type of the 
modal is unambiguous and the appropriate lexical entry 
can be retrieved. The creation of appropriate predicates 
to express the variety of modal statements is the task of 
the DRT module. 
4 THE QUERY' SYSTEM 
4.1 OPEN AND CLOSED WORLD ASSUMPTIONS 
It is well known that negation-as-failure is a sound 
extension of SLD resolution only when the database is 
Computational Linguistics, Volume 15, Number 3, September 1989 163 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
complete, i.e., when it represents a closed world (Lloyd 
1984). Since our databases will always include some 
predicates about which we have only incomplete infor- 
mation, we cannot assume a completely closed world. 
The open world assumption, though, makes it unsound 
to use the very useful negation-as-failure rule. Fortu- 
nately, it is well known that we can restrict the use of 
this rule to just those predicates known to be complete, 
keeping an open world assumption for other predicates. 
We accordingly specify that some of our general knowl- 
edge comprises a complete, closed world in the appro- 
priate sense, but we do not make this assumption about 
textual knowledge. 
4.2 FUNCTIONING OF THE QUERY SYSTEM 
Queries are handled just like text up through conversion 
to FOL. The FOL form of the query is then passed to 
REASONER, which decides which database is the most 
likely source of the answer. REASONER can access 
the textual database, the verb and noun generic data- 
bases, and the ontology. The reasoning is complex and 
dependent on whether the query form is ontological, 
factual, or generic. A search sequence is then initiated 
depending on these factors. The form of the answer 
depends on the search sequence and the place where the 
answer is found. 
4.2.1 ANSWERS 
The form of the answer depends on the reliability of the 
knowledge encoded in the database where the answer is 
found. The text is considered authoritative. If an answer 
is found in the text, search is terminated. The ontology 
is considered a closed world (see discussion above). 
This means that yes/no ontological questions are an- 
swered either "yes" or "no." The textual and generic 
databases are considered an open world. If an answer is 
not found, and no further search is possible, the system 
concludes, "I don't know." Answers found in the 
generic databases are prefaced by "Typically," for 
information found in the first list (of typical features) or 
"Inherently," for answers found in the second list (of 
inherent features). 
4.2.2 QUESTION TYPES 
An ontological question is in a copular sentence with a 
non-terminal ontological node in the predicate. 
56. Ontological Questions: 
Is a man human? 
Is the man a plant? 
A factual question is one couched in the past tense, 
present progressive, and/or where the subject is specific 
(a name or a definite NP). Specific NPs are assumed to 
have already been introduced into the system. Our 
simplified definition of generic question is one which 
contains an inherently stative verb or a non-stative verb 
in the simple present combined with a non-specific 
subject (indefinite NP).li 
57. Factual Questions: 
Did John buy a book? 
Is the man happy? 
Who bought the book? 
Generic Questions: 
Does a man buy a book? 
Does the man love horses? 
Ontological questions are answered by looking in the 
ontological database only. If an answer is not found, the 
response will be "no" if the query contained an onto- 
logical predicate (such as PLANT or ANIMAL) be- 
cause the ontology is a closed world. 
58. Text: John is a man who bought a book. 
Is a plant living?--Yes. 
Is the man an animal?--Yes. 
Is the man a plant?----No. 
Factual queries (non-generic questions) go to the textual 
database first. If an answer is not found, then the 
generic knowledge is consulted. If an answer is found 
there, the appropriate response (Typically.., Inher- 
ently..) is returned. Otherwise the response is, "I don't 
know." 
59. Text: John is a man who bought a book. 
Did John buy a book?---Yes. 
Who bought a book?--John. 
Is John the President?mI don't know. 
Does John wear pants?---Typically so. 
Where did John buy the book?--Typically, in a 
store. 
Generic: queries go only to the generic database. 
60. Does a man wear pants?--Typically so. 
What is the function of a lawyer?--Inherently, 
represents clients. 
Is an apple red? Typically so. 
Does a man love flowers?--I don't know. 
Where does a man buy a book? Typically, in a 
store. 
Who buys a book?---Inherently, a sentient. 
In addition to these general rules, there is special 
handling for certain types of queries. Questions of the 
form "Who is . . ." and "What is . . ." are answered 
by finding" every predicate in any data base that is true 
of the questioned entity. For example, in any of the 
examples above, the question "Who is John?" would 
trigger a response that includes a list of all the nodes in 
the ontology which dominate the position where 
"John" is attached plus the information that John is a 
man, is tall, and bought a book. The question "What is 
a vehicle?" would trigger only the list of ontological 
nodes because there is no specific vehicle in the domain 
of this example. Questions such as "Who buys a 
book?" and "What is driven?" are answered by stating 
selectional restrictions on the verb--Inherently, a sen- 
tient buys a book, and Inherently, a vehicle is driven. 
Finally, it is possible to override the generic informa- 
tion in specific cases while still retaining the capability 
of accessing the generic information later, as the follow- 
ing example shows. 
164 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
61. What color is an airplane?mTypically, white. 
John bought an airplane.--OK. 
What color is the airplane. - The text does not 
say, but typically white. 
The airplane is red.roOK. 
What color is the airplane?~The text says red. 
What color is an airplane?--Typically, white. 
Thus the system uses default logic (Reiter 1980). 
REASONER, therefore, is sensitive to a number of 
factors which make the system seem to understand the 
queries in a natural way. The responses generated also 
reflect the continuum of reliability of information which 
is available to a human reasoner. A flow chart of the 
search strategies in REASONER is shown in Figure 3. 
Ontologicol Question? 
0o oo0,l \[ I 
, ^oswer" I I ^nswer' 
Yes No i rextuolDB I I BUCCO~ 
I r 'r'"°'°' I ~ 
uooeod 
Try Inherent Answer:. 
DB Typico y...J 
fall \["~uceeed ~ucceed 
i i r, lnhnnti I 
I ~ ucceed 
Answer: Answer:. 
I don't know Inherent y... 
Figure 3. The Query System 
5 NAIVE SEMANTICS AND DISCOURSE PHENOMENA 12 
Most computational treatments of discourse phenom- 
ena acknowledge the role of world knowledge in ana- 
phora resolution, temporal reasoning, and causal rea- 
soning (Reichman 1985; Grosz and Sidner 1986; Wada 
and Asher 1986). However, in the past the only method 
for encoding and incorporating world knowledge in- 
volved writing a detailed script for every real-life situ- 
ation, directly encoding the probable sequence of 
events, participants, and so forth (Schank and Abelson 
1977). This section will demonstrate that word level 
naive semantics offers a principled, transportable alter- 
native to scripts. NS is a powerful source of information 
in discourse reasoning. Along with syntactic, composi- 
tional semantic, and discourse cue information, NS can 
be used to reason heuristically about discourse and 
drive many of the inferences drawn by people when 
they read a discourse. The role of syntax and composi- 
tional semantics will be underplayed in what follows, 
only because these contributions have been thoroughly 
treated by others (Reinhart 1982; Asher and Wada 1988; 
Kamp 1981; Grosz and Sidner 1986; Reichman 1985; 
Webber 1985). 
5.1 ANAPHORA 
In anaphora resolution, syntactic constraints, accessi- 
bility (in the sense of Kamp 1981), and discourse 
segmentation work in concert to limit the number of 
antecedents available to an anaphoric pronoun or defi- 
nite NP. However, it is clear that the resultant saliency 
stack can end up with more than one member (Asher 
and Wada 1988). It is at this point in the reasoning that 
naive inference is required. Consider the following. 
62. Levine's friend is a lawyer. He won his case for 
him. 
Syntactic rules exclude a reading in which "he" and 
"him" co-refer. Since both Levine and the lawyer are 
possible antecedents of all of the pronouns and there- 
fore are both in the saliency stack, the following read- 
ings remain: 
63. i. Levine won Levine's case for the lawyer. 
ii. Levine won the lawyer's case for the lawyer. 
iii. The lawyer won Levine's case for Levine. 
iv. The lawyer won the lawyer's case for Levine. 
Reading (iii) is the one people select. Generic knowl- 
edge of "lawyer" suffices to make this selection. The 
generic representation of "lawyer" in (9) includes in- 
formation that lawyers argue cases. An inspection of 
generic knowledge for "argue" reveals that one goal of 
arguing is winning. Using these two facts, the system 
can infer that the lawyer won, so the lawyer is the 
subject of the sentence. Thus the benefactee, by dis- 
jointness, must be Levine. Now the feature of "lawyer" 
that says that lawyers represent their clients could be 
used to figure that the case is Levine's. 
Turning to definite anaphora, a definite description 
often has an implicit antecedent in the discourse, rather 
than an explicit or deictic one. Unless world knowledge 
is used, it is impossible to recover these. Consider the 
following. 
64. Levine's trial was short. The testimony took only 
one day. 
A generic representation of "trial" is shown below. 
Using it, the antecedent of "testimony" in (65) can be 
identified as a part of the trial. 
65. trial- 
Typically, in a trial first there are oaths, state- 
165 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Comrnonsense Reasoning with Text 
ments and testimony, then the judge instructs the 
jury, the jury deliberates and announces a ver- 
dict, and the judge pronounces the sentence. 
There are roles of witnesses and spectators. A 
trial lasts about three days. 
Inherently, a trial has roles of judge, clerk, bailiff, 
attorneys, plaintiff, and defendant. The goal of a 
trial is to settle a dispute or determine the guilt or 
innocence of a defendant. 
An interesting subset of definite anaphora is definite 
event anaphora (Asher 1988). In (66), we know that 
"decision" refers to an event, because it is a deverbal 
nominal. Generic knowledge for deverbal nominals is 
the same as for verbs. So we know that there is some 
definite SOCIAL MENTAL event. Both commenting 
and sentencing are SOCIAL MENTAL verbs. Generic 
knowledge of the verb "sentence" includes knowledge 
that a sentencing is enabled by a decision. This can be 
used to infer that the antecedent of "the decision" is the 
sentencing event reported in the first sentence, rather 
than the commenting event. Thus e 4 = e I. 
66. (el) The judge sentenced Levine to a short term. 
(e2) He commented that Levine had been coop- 
erative. 
(e3) The decision (e4) surprised an attorney. 
If the generic knowledge has an implication which is 
similar to the event nominal, the correct antecedent can 
be inferred. The resulting DRS is shown in (67). 
67. 
and speech time (now). In a sequence of simple past 
tense clauses, the reference time is updated each time. 
So in text (66), the temporal equations look as follows. 
68. rl < now. 
el ~ rl 
r 1 ~ r 2 
e2 ~ r2 
r 2 .~ r 3 
e 3 C I" 3 
However, the reference time is not always updated by a 
simple past tense verb. In addition to the effects of 
clause aspect, which will be discussed below, common- 
sense knowledge affects temporal reasoning. Two 
events reported in a sequence of simple past tense 
clauses can overlap in time, or the second event (in 
textual sequence) can occur before the first. Naive 
semantic representations are sufficient to assign these 
relations correctly in many cases. The typical implica- 
tions of events in verb representations can be used to 
infer overlap. Consider the following discourse. 
69. (el) Levine made a statement. (e2) He said he was 
sorry. 
We know that a "statement" is a deverbal nominal of 
"state," and that the goal of "state" and of also "say" 
is communicating ideas. This knowledge is used to infer 
that e 2 C e 1. 
The regular temporal presuppositions of implica- 
tional features on verbs are very powerful in inferring 
the relative order of events. An event e i must occur 
before an event ej in order to cause ej. 
Xl, x2, a 1, e I, x 3, p, e 4, x 3, a2, e2, e3 
judge(xl) 
levine(x2) 
term(a0 
short(a0 
el sentence(x~,x2) 
to(el,a0 
he(x3) 
e2 comment(xa,p) 
P: s2 
s2 cooperative(x2) 
X 3 = X I 
decision(e3) 
attorney(a2) 
e2 surprise(ea,a2) 
e 3 = e 1 
5.2 TEMPORAL REASONING 
Temporal reasoning involves the assignment of relation- 
ships among the times of the events reported in the 
discourse. Following the Reichenbachian approach to 
tense found in Partee (1984), there are three elements to 
temporal reasoning: event time (el), reference time (ri), 
cause(el,e2) e2 before el 
enable(el,e2) e 2 before e l 
goal(el,e2) e 2 after e ! 
consequence(e I,e2) e 2 after e l 
whnext(el,e2) e 2 after e 1 
when(el,e2) e 2 overlap e 1 
where(el,e2) e2 overlap e I 
how(el,e2) e 2 overlap el 
Table 10. Temporal Presuppositions of Verb Implications 
Table 10 lists the temporal presuppositions of several 
generic: features of verbs. This works well when one of 
the implications of a verb mentions another verb (or 
related verb) in the text, and tense or adverbial modifi- 
ers do not give clues as to the temporal relations. In 
(70), the fact that e2 is a typical cause of el, can be used 
to infer that e 2 occurred before e l. 
70. (ea) Levine was found guilty. (e2) He broke the 
law. 
Similarly, in (71), the fact that buying typically takes 
place in stores, can be used to infer that e 2 overlapped 
el in time. (The stativity of the verb in the second 
sentence is also a weak, but insufficient indicator of the 
temporal relationship between the sentences). 
Computational Linguistics, Volume 15, Number 3, September 1989 166 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
71. (el) Levine bought a book. (e2) He was in a store 
on 5th Ave. 
Temporal properties of nouns also require naive seman- 
tics. Role terms have a temporal element (Enc 1987). A 
term such as "President of the U.S." has a DURA- 
TION feature in the naive semantic representation. This 
enables appropriate inferences concerning the occu- 
pants of roles. In (72), the system can infer that the 
individual who declared war on insider trading is prob- 
ably not the same as the one whose office indicted 
Levine. 
72. The Attorney General declared war on insider 
trading in 1964. The Attorney General's office 
indicted Levine six months ago. 
Another way in which naive semantic representations 
can be used in temporal reasoning has to do with the 
assignment of tense to deverbal nominals. In (73), the 
nominal "violations" refers to events which can be 
placed in time before the sentencing. Naive semantic 
knowledge can be used to infer that the violations took 
place before the sentencing, because violations are 
illegal, sentencing is the fnal stage of a trial, a trial 
determines whether the defendant is guilty, and guilty 
parties have done something illegal. 
73. Levine, who engaged in massive insider trading, 
was sentenced to two years in prison. His viola- 
tions of the securities laws were shocking. 
Another way that naive semantics helps temporal rea- 
soning, comes from the assignment of coherence rela- 
tions using naive semantics. How this can be done is 
discussed below (Section 5.4). Given certain rhetorical 
relations between two clauses, certain temporal rela- 
tionships are indicated. Table 11 lists the temporal 
predications of certain coherence relations. The first 
group are relations which hold between events or states 
81 and 62, where 81 occurs before 62. In (74) the system 
can recognize that e I is before e2, once it assigns the 
relation Cause between them. 
74. (e0 Levine broke the law. (e2) He was indicted. 
The same thing would work if the sentences were in 
reverse order in the text. The second group in Table 1 1 
lists the coherence relations that indicate that events or 
states overlap in time. The third group lists those 
relations that require that the source event or state is in 
the speech time (now). 
5.3 CLAUSE ASPECT 
Clause aspect refers to the aspect of an entire clause. 
The classical example in (75) through (77) illustrates 
differing clause aspects with the same verb and tense. 
(75) is telic, while (76) and (77) are activity clauses. 
75. John pushed the cart under a shed. 
76. John pushed the cart under adverse conditions. 
77. John pushed the cart. 
Ciause-stative means not only the opposition between an 
inherently stative verb and an eventive verb (as in "be" 
vs. "hit"), but to the various ways in which a whole 
8 1 and 62---event or state reference markers 
r~ < rE--reference times 
I. t~ 1 C rl, 62 C r 2 Elaboration(Si,62), Cause(81,62), 
Goal(62,81) 
Evidence(62,80, 
Enablement(81,62), 
Comment(62,Sl), 
2. 81, 62 C_ r I Parallel(81,62), Contrast(81,62), 
Description(61,62), 
Qualification(81,62), 
Evidence(81,62) 
Generalization(81,62), 
Import(81,62), 
3. 62 C_ now Import (62,61) , Evaluation(62,81) 
Table 11. Tense and Coherence Relations 
clause can end up being stative, as with the presence of 
the progressive, or a number of other factors. Clause- 
relic means that the clause reports a change of state with 
a terminus. "John built the house" is clause-telic, while 
"John was building the house" is clause-stative. A telic 
clause has an ACHIEVEMENT or ACCOMPLISH- 
MENT verb not in the progressive, not in the simple 
present (which would be habitual, and with no modal 
(e.g., "John will build the house" is not telic). Clause- 
activity has to do with a clause which reports an event 
which has no terminus, and which has the sub-interval 
property (Bennett and Partee 1978), as "John ran." 
Naive semantic knowledge is used to assign clause 
aspect. In (75), naive semantic knowledge that a shed is 
a PLACE, and generic information of the relative sizes 
of carts and sheds, can be used to infer that the shed 
was a destination for the cart. An ACTIVITY verb such 
as "push," with a destination argument in the verb 
phrase results in a telic clause. This inference for (75) 
would not hold for (76). Similarly, an ACHIEVEMENT 
or ACCOMPLISHMENT verb indicates a TELIC 
clause, but other arguments can change them to AC- 
TIVITY (cf. Moens and Steedman 1987). For example, 
in (78) the clause is TELIC, while in (79) it is ACTIV- 
ITY, and in (80) it is ambiguous between TELIC and 
ACTIVITY. 
78. The prosecutor questioned the point, 
79. The prosecutor questioned the witness for an 
hour. 
80. The prosecutor questioned the witness. 
S.4 COHERENCE RELATIONS 
Coherence relations are handled in the KT system as 
added predicates in a cognitive DRS. In the DRS 
representing two clauses connected by a discourse cue 
word such as "because," a predicate cause(el,e2) is 
represented. Similarly, where the first event typically 
causes the second, the system guesses the cause rela- 
tion between the two event reference markers, and a 
Computational Linguistics, Volume 15, Number 3, September 1989 167 
Kathleen Dahlgren, Joyce McDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
cause predicate is introduced into the DRS, resulting in 
a cognitive (or inferred) DRS. Coherence relations are 
assigned using syntax, temporal relations, clause as- 
pect, discourse cues, and naive semantics. An algorithm 
for coherence relation assignment has been developed 
(Dahlgren 1988c, 1989). This section will illustrate only 
the contribution of naive semantics and will not delve 
into the complex problem of the interactions among the 
several sources of information. Grosz and Sidner (1986) 
argue that coherence relations are not a useful analytical 
tool because no clear, closed set of them has been 
discovered. However, there is ample psycholinguistic 
evidence that in constructing the interpretation of a 
text, and in recalling what it said, coherence relations 
are inferred and used by readers (Rickheit and Strohner 
1985). In terms of computational linguistics, coherence 
relations are useful for text summarization and rele- 
vance reasoning. In text summarization, only the gen- 
eral actions, not the elaborations, can be included in the 
summary. Descriptive and other background clauses 
can be ignored. Similarly, relevance can be inferred 
from the causal implications of events reported in a text. 
If a reader says that he or she wants to read about 
events that affect the construction industry, for exam- 
ple, and the typical consequence of some event in a text 
affects the construction industry, then that reader is 
interested in that text. 
The naive semantic representations of nouns and 
verbs contain sufficient information to handle a large 
number of cases in which world knowledge is required 
to structure the discourse. Generic representations of 
the typical implications of verbs such as cause, goal, 
enablement, and consequence are the very same infor- 
mation as coherence relations. Their content means, "If 
there was an event (or state) of VERBling, then it 
probably had as goal a later event (or state) of 
VERB2ing." For example, "If there was an event of 
buying, it probably had as goal a state of owning." 
Naive semantic representations contain generalizations 
about objects and actions which are common to a 
linguistic community, and thus explain the ability to 
understand a discourse without resort to particular 
scripts describing familiar real-world situations. 
Using generic and ontological representations de- 
rived from psycholinguistic data, coherence relations 
can be assigned. To infer goal(el,e2) for text (81), 
knowledge that "profit" is money can be used to relate 
(e2) to the the goal feature of "invest." 
81. (e0 John invested heavily. (e2) He made a huge 
profit. 
Some of the naive semantic representation of "invest" 
is shown below: 
82. Investing is typically lucrative and is accom- 
plished with money. Inherently, sentients do the 
investing with the goal of making money. 
Similarly, in (83), the generic entry for "insider trading" 
can be used to infer that Levine broke the law. The 
entry associated with reading 1 of the verb "charge," as 
shown in Table 9, includes information that a typical 
cause of charging someone is that that someone has 
committed a crime. Putting these two together, 
cause(el,e2) can be inferred. 
83. (e0 Levine engaged in insider trading. 
(e2) The government charged him with violations 
of the securities laws. 
The segmentation of discourse takes into account para- 
graphing, discourse cues such as Turning to .... In 
summary, clause aspect, temporal relations, and coher- 
ence relations. In this section we will briefly illustrate 
that nai, ve semantics is one source of information in 
discourse segmentation. In narrative, a clear distinction 
can often be made between segments consisting of 
sequences of actions that are the foreground of the 
narrative, and segments that provide the background or 
setting for the action. If the author does not give clear 
discourse cues of the switch to a setting or situation 
segment, the shift can be inferred using naive seman- 
tics. In our method, discourse segments are related to 
each other the same way as clauses (as in Mann and 
Thompson \[1987\] and Hobbs \[1985\]), so the relationship 
here is one of situation_activity(Seg2,Segl) where Segl 
is a sequence of actions. Consider (84). 
84. Levine engaged in insider trading at his firm. He 
was charged and found guilty of violations of the, 
securities laws. He was sentenced by Judge 
GoetteL Levine was happy at his firm. The audi- 
ence waited with baited breath to hear what 
Judge Goettel would say. 
In the text, there is a change of segment at "Levine was 
happy at his firm." The segment is a SITUATION- 
ACTIVITY segment. It describes what was going on 
when Levine was engaging in illegal practices. The 
change from a sequence of actions to a background 
segment is indicated by several factors, including the 
use of the stative and the place adverbial. Another 
factor is naive semantic knowledge of "firm." Working 
takes place at a firm, and this knowledge can be used to 
infer that "Levine was happy at his firm" refers to a 
long-term situation in which Levine was working. In 
that situation, he was happy. Thus the sentence is not 
about some specific action, but is a generalization about 
Levine's condition as a worker. Such a generalization 
indicates a change of segment from a sequence of 
actions to a SITUATION_ACTIVITY segment. 
6 CONCLUSION 
Naive semantics is a level of cognitive representation of 
concepts that can be discovered empirically and repre- 
sented in a principled way with FOL without resort to a 
special knowledge representation language. Because 
NS representations are linked to ordinary words and do 
not depend on a special knowledge representation level, 
they should be transportable from one text to another. 
The KT system demonstrates that these rich represen- 
168 Computational Linguistics, Volume 15, Number 3, September 1989 
Kathleen Dahlgren, Joyce MeDowell, and Edward P. Stabler, Jr. Knowledge Representation for Commonsense Reasoning with Text 
tations are powerful in resolving many of the large 
residue of ambiguities that remain after the work of a 
purely syntactic parser is completed. 
NOTES 
1. Dahlgren and McDowell are the main investigators. Stabler 
contributed the first-order logic and problem solver. 
2. Nominalized verbs are treated as verbal concepts. 
3. We are exlLremely grateful to Hajime Wada who wrote the 
original version of the DRT module to provide DRSs for a wide 
range of syntactic constructions. The present DRT module is an 
extension of his work. The lexical entries that form the NS data 
bases are the result of the careful, diligent efforts of Carol Lord, 
Robert Hagiwara, and Susan Mordechay. Susan Hirsh contrib- 
uted the programs that generate the FOL data bases and 
performed other programming tasks. 
4. The atoms subj, obj, obliq, pobj, which appear in the generic 
representations, signal which element of the sentence is to be 
accessed for the output response. In answering questions, the 
generic representations are mapped to a small set of canonical 
sentences whose slots are filled with elements from the input 
query. Consider the feature representation implies(merchandise 
(obj)), which is part of the generic entry for buy. If the query 
What is implied if a man buys a truck? is processed, since the 
direct object of buy in the query is truck, the response is the 
truck is merchandise. If the query had been What is implied if 
John buys a house?, the response would be the house is 
merchandise. 
5. The analysis work was originally reported in Dahlgren and 
McDowell (1986b). We review that here and also report on the 
implementation. 
6. For the most part, examples given in this paper are modified 
versions of actual sentences from the WSJ corpus that the KT 
researchers are using. 
7. For readability, the remaining generic entries will be shown in 
English paraphrase rather than as they are coded. 
8. The question arises as to how natural or likely such sentences 
would be in use. For example, would we be more likely to 
encounter a letter from all the lawyers rather than a letter from 
every lawyer. The standard answer, which we adopt, is that 
semantics is not a predictive theory. We can't tell what a person 
will say, but we have to be able to interpret whatever is said. We 
can restrict ourselves to likely expressions, but then we are 
putting ourselves in the position of predicting what is likely, and 
we might be wrong. We prefer to try to interpret what is 
possible, even if unlikely. 
9. A full theoretical discussion of the issues involved with medals 
can be found in McDowell (1987). 
10. The main predicate of a clause, whether it be a verb or a term 
that is the complement of the copula, carries a DR-theoretic 
event-type reference marker as its tense argument. Other pred- 
icates carry an argument which is linked to tense, but which is 
not a reference marker in DR-theoretic terms. 
11. Actually, sentences interpreted as generics in English have a 
complex combination of the following features: indefinite NPs, 
present tense, copula, and/or inherently stative verb (but not the 
progressive). Not all of these features are always present in all 
generically interpreted sentences. 
12. In ~Lhis and the following sections we report on work in progress 
that is not yet fully implemented. 
Computational Linguistics, Volume 15, Number 3, September 1989 170 

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