Exploiting Lexical Regularities in 
Designing Natural Language Systems 
Boris Katz 
Artificial Intelligence Laboratory 
Massachusetts Institute of Technology 
Cambridge, MA 02139 
Beth Levin 
Department of Linguistics 
Northwestern Unlversigy 
Evanston, IL 60208 
They've a temper, some of them--particularly 
verbs: they're the proudest--adjectives you 
can do anything with, but not verbs-- 
however, I can manage the whole lot of them\[ 
-- Lewis Carroll, Through the Looking-Glass 
Abstract 
This paper presents the lexical component of the STAttT Ques- 
tion Answering system developed at the MIT Artificial Intelli- 
gence Laboratory. START is able to interpret correctly a wide 
range of semantic relationships associated with alternate expres- 
sions of the arguments of verbs. The design of the system takes 
advantage of the results of recent linguistic research into the 
structure of the lexicon, allowing START to attain a broader 
range of coverage than many existing systems while maintain- 
ing modular organization. 
1. Introduction 
If asked "Did Sally eat?" after having been told that Sally ate 
a pear, speakers of English would not hesitate to answer "Yes". 
But we would not expect English speakers to answer "Yes" if 
asked "Did David dress?" after being told that David dressed 
the baby. Here the appropriate answer would be "I don't know". 
Computational linguists engaged in building Question-Ans- 
wering systems should find these examples thought-provoking. 
Two sequences consisting of a statement followed by a question 
which appear to be parallel syntactically (transitive use of a 
verb in the statement, intransitive use of the same verb in the 
question) elicit quite different responses. The simple syntax of 
these pairs is unlikely to pose a challenge for the parsers used in 
most existing systems. The problem is that the intransitive uses 
of the two verbs, eat and dress, receive very different interpre- 
tations. Thus the intransitive use of eat found in the question 
"Did Sally eat?" implies the existence of an understood but un- 
expressed 'object that is interpreted as a prototyplcal type of 
food or a meal: 
(1) Sally ate a pear. ~ Sally ate. (i.e., Sally ate some food.) 
The question "Did David dress?" on the other hand does not 
mean ~Did David dress something one typically dresses?', it 
means 'Did David dress himself?': 
(2) David dressed the baby, =7~ David dressed (i.e., David 
dressed himself.) 
Natural language systems should be able to recognize that the 
relationship between transitive and intransitive dress is not the 
same as that between transitive and intransitive eat. 
A large number of English verbs have both transitive and 
intransitive uses. Interchanges parallel to the one described for 
eat are possible with a wide range of verbs: 
(3) Jessiea typed a letter. Did Jessica type? Yes. 
(4) Gabriella swept the floor. Did she sweep? Yes. 
(5) Miriam read the book. Did Miriam read? Yes. 
But the behavior of the verb dress is not exceptional. Another 
set of verbs including bathe, change, shave, shower, and wash 
behave like it. For example, these verbs show the same entail- 
ments as dress: 
(6) Carla bathed the dog. :=~ Carla bathed (i.e., Carla bathed 
herself.) 
(7) Jill washed the sweater. =fi~ Jill washed (i.e., Jill washed 
herself.) 
(8) Peter shaved Tom. ~ Peter shaved (!.e., Peter shaved 
himself.) 
The different relationships between transitive and intransi~ 
give uses of verbs cannot be ignored in the design of a natural 
language system and its lexical component, The most obvious 
way to handle these relationships is to add information to the 
lexical entries of each verb with transitive and intransitive uses. 
While such an approach is viable when a system has a smM1 lex~ 
icon~ it becomes less tractable as the lexicon grows larger since 
it requires a tremendous increase in the amount of idiosyncratic 
information which must be registered in the entry of ea(-h verb. 
The examples discussed so far illustrate just a few of a 
wide range of relationships between alternate expressions of the 
arguments of verbs that must be correctly interpreted by any 
natural language system that alms at substantial coverage of 
English. We believe that what is required in order to imple- 
ment a system that meets these demands is an understanding 
of English lexical organization. For this reason we draw on re- 
cent theoretleal linguistic investigations into the lexical knowl- 
edge possessed by native speakers of English carried out by the 
MIT Lexicon Project (Rappaport, Levin, and Laughren \[1988\], 
Levin \[1985\], Hale and Keyser \[1986\], Levin and Rappaport \[to 
appear\]). These studies have established a range of semantic- 
syntactic interdependencies exhibited by semantically coherent 
classes of verbs and have identified a number of essential classes 
of verbs, as well as the central properties characterizing verbs 
of each type. 
The results of this work have been used in the design of 
a lexical component for the START natural language system 
developed at the MIT Artificial Intelligence Laboratory (Katz 
\[1988\]). In this paper we show how these resttlgs allow STAtt2\[' 
to attain a broader range of coverage than most existing systems 
while maintaining modular organization. 
316 
2o Assu\[nptlons about Lexicai Organization 
A verb d,~notes an action, state, or process involving one or 
more participants, which we refer to as the arguments of the 
verb. Solae verbs may express their argtunents in more than one 
way, sometimes wi~h slightly different semantic interpretations. 
We say th*~t such verbs patriciate in argument alternations. We 
have seen that certain verbs have both transitive and intransitive 
uses, and That the relationship between the uses is not uniform 
across all eerbs; rather, it is a property of the verb involved. 
We say theft such verbs participate in transitivity alternations, 
a subclass of argument alternations. So far, we have seen the 
indefinite object alternation, in (9), and the reflexive alternation, 
in (10): 
(9) Sally ~de a pear. :~:,-,~ Sally ate. (i.e., Sally ate some food.) 
(10) David dressed the baby. ::~ David dressed. (i.e., David 
dressed himself.) 
These are only two of about a dozen transitivity alternations 
found in English. (See Atkins, Keg/, and Levln \[1986\], Hale and 
Keyser It9~t6\], Levin \[1985\] for a description of various other 
altern~tion.~l.) A question that a system designer might ask is 
whether the argument Mternations a verb participates in are 
predictable or whether they are merely an idiosyncratic property 
of that w~rt,. This question is clearly also of linguistic interest. 
Argument alternations, including transitivity alternations, 
have receiw~d considerable attention from theoretical linguists. 
It tin'as out that each alternation is associated with particular 
semantic types of verbs. Thus the verbs participating in the 
indefinite object alternation ate all activity verbs; most of them 
describe typical occup.ations: a 
(11) drink, eat, file, iron, plow, read, sweep, type, write .... 
The verbs pa~rtieipating in the reflexive alternation are all verbs 
of grooming: 
(12) bathe, brnsh, change, comb, dress, shave, shower, wash, ... 
Studies of phenon:ena such as those described here reveal 
that Englis:h verbs are organized into classes on the basis of 
shared components of meaning. The members of these classes 
share certai:: syntactic properties, specifically properties con- 
cerning the possible expressions of their arguments. To state 
this differently, certain linguistically relewnt aspects of a verb's 
meaning serve as a pointer to its place in the organizational 
scheme of English verbs. Once this place is identified, various 
syntactic properties of a verb can be determined. 
For linguists working on the lexicon the study of alter- i 
nations can provide insight into linguistically relevant aspects 
of meaning due to the interdependency between the syntactic 
and semantic properties of a verb. (For instance, see Hale and 
Keyser \[1987\] for a discussion of another transitivity alternation, 
the middle ttlternation, from this perspective). For eompttta- 
tional linguists this characteristic of lexicM organization sug- 
gests a modtflar system design: many lexical properties can be 
associated with verb classes, and need not be registered in the 
lexical entries of individual verbs, which can simply indicate 
class rnember~hip. These ideas are incorporated into the design 
of tb.e START system, as we describe in detail in the remainder 
of the paper. 
1 In certain circumstances, a much wider range of English transitive 
verbs show an intransitlve use with an indefinite object interpre- 
tation: when they are used by people whose activity involves the 
action denoted by the verb to describe the activity. For exam- 
ple, the verb stuff does not usually occur without an object, ~She 
stuffs, but if this verb is used to describe the activity of someone 
Who stuffs pillows in a pillow-factory, then it would be M1 right to 
use the verb :ntransitively. 
3. An Overview of the START system 
The START natural language system (SynTactic Analysis us- 
ing Reversible Transformations) consists of two modules which 
share the same Grammar (see Katz \[1980\], Katz and Winston 
\[1982\]). The understanding module analyzes English text and 
produces a knowledge base which incorporates the information 
found in the text. Given an appropriate segment of the knowl- 
edge base, the generating module produces English sentences. A 
user can retrieve the information stored in the knowledge base 
by querying it in English. The system will then produce an 
English response. 
START has been used by researchers at MIT, Stanford 
University, and the Jet Propulsion Laboratory for constructing 
and querying knowledge bases using English. (See, for exam- 
ple, Winston \[1982\], \[1984\], Winston, Binford, Katz, and Lowry 
\[1983\], Doyle \[1984\], Katz and Brooks \[19871). 
START rearranges the elements of the parse tree it con- 
structs into embedded ternary expressions (T-expressions) by 
tying together the three most salient parameters of a sentence, 
the subject, the object, and the relation between them, <subject 
relation object>. For instance, the sentence 
(13) Gabriella might buy some stickers 
will result in the T-expression <Gabriella buy stickers>. 
Certain other parameters (adjectives, possessive nouns, preposi- 
tional phrases, etc.) are used to create additional T-expressions 
in which prepositions and several special words serve as rela.- 
finns. 
The remaining parameters--adverbs and their position, ten- 
se, auxiliaries, voice, negation, etc.--are recorded in a represen. 
rational structure called history. The history has a page per- 
taining to each sentence which yields the given T-expression. 
When we index the T-expression in the knowledge base, we 
cross-reference its three components and attach the history H 
to it. One can thus think of the resulting entry in the knowl- 
edge base as a "digested summary" of the syntactic structure of 
English sentences. 
In order to handle embedded sentences, START allows any 
T-expression to take another T-expression as its subject or ob-- 
jeer. START can analyze and generate sentences with arb:trar- 
ily complex embedded structures. 
We conclude our description of START with a brief overview 
of how the system answers questions. Questions are requests for 
information from START's knowledge base. In order to answer a 
question START must translate the question into a T-expression 
template which can be used to search the knowledge base for 
T-expressions which contain infornmtion relevant to providing 
an answer to the question. We illustrate the actual question~ 
answering process with an example. Suppose that as a result 
of analyzing and indexing a text containing sentence (14), the 
knowledge base contains T-expression (15): 
(14) Mary presented Paul with a gift. 
(15) <<Mary present Paul> with gift> 
Now suppose that a user asks START the following wh-question: 
(16) Whom did Mary present with a gift? 
In the context of.(14) the answer is Paul. In order to deter- 
mine this, the system must first turn the question into a T- 
expression template that can be used to search the knowledge 
base. The first step in this process is to undo the effects of the 
~h-movement transformation that is used to create English wh- 
questions. To do this, START must find the place in sentence 
(16) that the wh-word whom came from and then insert the 
wh-word in this position: 
317 
(17) Mary presented whom with a gift. 
Next the language understanding system leads sentence (17) 
through the same flow of control as any other declarative sen- 
tence and produces the following T-expression which serves as 
a pattern used to query the knowledge base: 
(18) <<Mary present whom> with gift> 
Treating whom as a matching variable, the system feeds T- 
expression (18) through a matcher in order to determine whether 
there is anything in the knowledge base that matches (18). The 
marcher finds the T-expression created from (14): 
(19) <<Mary present Paul> with gift> 
and the language generation system then uses this T-expression 
to produce the English response to question (16): 
(20) Mary presented Paul with a gift. 
START handles yes-no questions in a similar fashion. Sup- 
pose that START had been asked the yes-no question 
(21) Did Mary present Paul with a gift? 
As in the wh-case, START would turn this question into a 
T-expression template that could be matched against the T- 
expressions in the knowledge base. The difference between yea- 
no and wh-questions is that the T-expression template generated 
by a yes-no question would contain no variables. The question 
above would generate the template <<Mary present Paul> 
with gift> which would match against (19), allowing the sys- 
tem to answer: 
(22) Yes, Mary presented Paul with a gift. 
4. Introducing S-rules 
Since T-expressions in the START system are built using the 
pattern <subject relation object> at every level of embed- 
ding, they mimic the hierarchical organization of English sen- 
tences. As a consequence, sentences differing in their surface 
syntax but close in meaning are not considered similar by the 
system. For example, given sentence (23) as input, START will 
create an embedded T-expression (24), whereas a near para- 
phrase, sentence (25), will generate T-expression (26): 
(23) Mary presented Paul with a gift 
(24) <<Mary present Paul> with gift> 
(25) Mary presented a gift to Paul 
(26) <<Mary present gift> to Paul> 
Speakers of English know that sentences (23) and (25) both de- 
scribe a transfer of possession. In both sentences, the gift is 
the transferred object, Paul is the recipient of this object, and 
Mary is the agent of the transfer~ despite different syntactic re- 
alizations of some of these arguments. It seems natural that 
this kind of knowledge be available to a natural language sys- 
tem. However, START, as described so far, does not consider 
T-expressions (24) and (26), which are associated with these 
sentences, to be similar. 
The difference in the T-expressions becomes particularly 
problematic when START is asked a question. An example will 
clarify this point. Suppose the input text contains only one 
present sentence, (27), and the knowledge base contMns only 
the corresponding T-expression, (28): 
(27) Mary presented Paul with a gift 
(28) <<Mary present Paul> with gift> 
Now suppose the user asked the following question: 
(29) To whom did Mary present a gift? 
318 
Although a speaker of English could easily answer this question 
after being told sentence (27), START would not be able to 
answer it. This question presents a problem for START because 
T-expression (30) produced by question (29) will not match T- 
expression (28). 
(30) <<Mary present gift> to whom> 
START is unable to answer such questions because it is 
unaware of the interactions between the syntactic and semantic 
properties of verbs. This limitation is a serious drawback since 
interactions similar to the one just described pervade the English 
language and, therefore, cannot be ignored in the construction 
of a natural language system. 
The present example illustrates that START needs informa- 
tion that allows it to deduce the relationship between alternate 
realizations of the arguments of verbs. In this instance, we want 
START to know that whenever A presents B with C, then A 
presents C to B. We do this by introducing rules that make 
explicit the relationship between alternate realizations of the 
arguments of verbs. We call such rules S~rules (where S stands 
for both Syntax and Semantics). Here is the S-rule that solves 
the problem caused by the verb present: 
(31) Present S-rule 
If <<subject present object1> with object2> 
Then <<subject present object2> to objectl> 
S-rules are implemented as a rule-based system. Each S-rule 
is made up of two parts, an antecedent (the IF-clause) and a 
consequent (the THEN-clause). Each clause consists of a set of 
templates for T-expressions, where the template elements are 
filled by variables or constants. For example, the Present S-rule 
contains three variables, subject, object1, object2, which are used 
to represent the noun phrases in the T-expressions. This rule 
also contains three constants, present, with, and to, shown in 
boldface. The Present S-rule will apply only to T-expressions 
which involve the verb present and which meet the additional 
structural constraints. 
S-rules operate in two modes: forward and backward. We 
describe both modes of operation briefly, although in this pa- 
per we concentrate on S-rules operating in the backward mode, 
since this mode is most useful in a Question-Answering natural 
language system. 
When triggered by certain conditions, S-rules in the forward 
mode allow the system to intercept T-expressions produced by 
the understanding module, transform or augment them in a way 
specified by the rule, and then incorporate the result into the 
knowledge base. For instance, if the Present S-rule is used in the 
forward mode, as soon as its antecedent matches T-expression 
(32) produced by the understanding module, it creates a new 
T-expression (33) and then adds it to the knowledge base: 
(32) <<Mary present Paul> with gift> 
(33) <<Mary present gift> to Paul> 
Now question (29) can be answered since T-expression (30) as- 
sociated with this question matches against T-expression (33). 
The generating module of START responds: 
(34) Mary presented a gift to Paul. 
All additional facts produced by the forward S-rules are in-. 
stantly entered in the knowledge basel The forward mode is 
especially useful when the information processed by START is 
put into action by another computer system because in such a 
situation START ought to provide the interfacing system with 
as much data as possible. 
In contrast, the backward mode is employed when the user 
queries the knowledge base. Often for reasons of computa- 
tional e/l~cleney, it is advantageous not to incorporate all in- 
ferred knowledge into the knowledge base immediately. S-rules 
in the bac:kward mode trigger only when a request comes in 
which cannot be answered directly, initiating a search in the 
knowledge base to determine if the answer can be deduced from 
the available information. For example, the Present S-rule used 
in the backward mode does not trigger when sentence (27) is 
read and T-expression (28) is produced by START. The S-rule 
triggers only when question (29) is asked since this question 
cannot be answered directly. 
5. The I,exical Component of START 
In order to lmderstand an English sentence, the START system 
needs to have morphological, syntactic, and semantic informa- 
tion about the words in the sentence. All the words that the 
system is aware of, along with information about their part of 
speech, inflection, gender ~, number, etc. are stored in the Lex- 
icon. Virtually every branch of START uses the Lexicon to 
accomplish i~s task. In this section we discuss the way in which 
the Lexicon extends the system's ability to dcal with semantic- 
syntactic interdependencies. We show that the Lexicon provides 
a place where a verb's membership in a semantic class can be 
registered, a)lowing more general S-rules to be stated. 
To exantine how lexieal information about verb classes may 
be utilized by the S-rules, we introduce another argument al- 
ternation in English, the property-factoring alternation (Van 
Oosten \[1980\]). Consider the following sentence: 
(35) Paul surprised the audience with his answer. 
An English speaker knows that sentence (35) can be paraphrased 
(36) Paul's answer surprised the audience. 
Notice that in (35), the subject brings about the emotional reac- 
tion (surprise) by means of some property expressed in the with 
phrase. Sentence (36) describes the same emotionM reaction as 
in (35) but in (36) the property and its possessor are collapsed 
into a single noun phrase. 
Suppose that after sentence (35) is typed into the computer, 
we ask: 
(37) Did Paul's answer surprise the audience? 
While a speaker of English would know that the answer to this 
question is Yes, this reply is not obvious to START since T- 
expressions :related to sentence (35) and question (37) are very 
different: 2 
(38) <<Paul surprise audience> with answer> 
(39) <answer surprise audience> 
Extending t:he approach taken to the example with the verb 
present in section 4, we could formulate a simple S-rule that 
could be used to answer question (37). The Surprise S-rule (40), 
like the Present S-rule, makes explicit the relationship between 
the alternate realizations of the arguments of the verb surprise: 
'2'1'o simplify the exposition we do not show the T-expression de- 
scribing the relation between the property (answer) and its pos- 
sessor (P,~'.l). 
(40) Surprise S-rule 
If <<subject surprise object1> with object2> 
Then <object2 surprise object1> 
In taking this approach we are explicitly associating the property- 
factoring alternation with the verb surprise; we are assuming 
that it is an idiosyncratic property of the verb. 
Formulating a special purpose S-rule which applies only to 
the verb surprise does not seem to be the best solution to the 
problem. Surprise is only one of many verbs which exhibit the 
property-factoring alternation. This alternation holds of a large 
class consisting of over one hundred verbs, among them 
(41) anger, annoy, embarrass, frighten, impress, please, ... 
For example: 
(42) Miriam amused Jessica with her performance. 
(43) Miriam's performance amused Jessica. 
(44) Gabriella scared the turtle with a sudden movement. 
(45) Gabriella's sudden movement scared 'the turtle. 
These verbs also share a certain semantic property: they all 
denote emotional reactions. For this reason we identify a class 
of emotional-reaction verbs and say that the property of the verb 
surprise responsible for the alternation shown in (35) and (36) 
holds for all verbs that comprise the emotional-reaction class. 3 
Once we have tied the ability to participate in the property- 
factoring alternation to a particular class of verbs, we no longer 
need to indicate this property in the lexical entry of each verb 
in the class or write verb-specific S-rules, such as the Surprise 
S-rule. Rather, we can associate the alternation with the class 
and then simply indicate in the lcxical entry of a verb whether 
it belongs to tlfis class. That is, we augment a verb's lexical 
entry with an indication of its semantic class membership. For 
instance, we would register in the entry for surprise that it is a 
member of the emotional-reaction class. 4 
(46) (surprise :verb :emotional-reaction) 
Now instead of writing a number of verb-specific S-rules, we can 
write a single general S-rule which triggers not only on the verb 
surprise, but on any verb fl'om the emotional-reaction class: 
(47) Property-factoring S-rule 
If <<subject verb object1> with object2> 
Then <object2 verb object1> 
Provided verb E emotional-reaction class 
The revised S-rule contains a PROVIDI~D clause which specifies 
the class of verbs to which the rule applies, ensuring that it 
applies to the emotional-reaction verbs. 
When question (37) is asked, the Property-factoring S-rule 
(used in the backward mode) will trigger, since the T-expression 
(48) <answer surprise audience> 
3These verbs have been the subject of extensive study ill the linguis- 
tic literature because of these and other characteristic properties 
that set this class apart. (See Postal \[1971\], Pesetsky \[1987\], Bel- 
letti and R.izzi \[1986\], Grimshaw \[to appea-\], and many others). 
This class of verbs which take the experiencer argument (the per- 
son experiencing the emotions) as object should be distinguished 
from a second class of verbs of psychological state which take the 
experlencer argument as subject. The latter class which includes 
verbs like admire, detest, esteem, hate, and like will not be dis- 
cussed in this paper. 
4Irrelevant details have been suppressed in this lexical entry. 
519 
produced by the question matches the THEN-part of the rule, 
and furthermore, the verb surprise belongs to the emotional- 
reaction class. The correct answer to question (37) is deduced 
when the appropriately instantiated IF-part of the S-rule is 
matched to T-expression (38) found in the knowledge base. Here 
is how START responds: 
(49) Yes, Paul's answer surprised the audience. 
The PROVIDED restriction of S-rule (47) not only allows 
the rule to apply to verbs of the appropriate semantic type, 
but it also prevents the rule from applying to verbs that do 
not display the property-factoring alternation. For instance, 
the verbs surprise and present can express their arguments in 
a similar fashion--both are found in the context \[NP V NP 
with NP\], but they differ in the other realizations of their arg-u- 
ments. Specifically present does not participate in the property- 
factoring alternation, as (50) shows, nor does surprise partic- 
ipate in the alternation that present participates in, as (51) 
shows: 
(50) Mary presented Paul with a gift. 
*Mary's gift presented Paul. 
(51) Paul surprised the audience With his answer. 
*Paul surprised his answer to the audience. 
In the absence of the PROVIDED clause, the Property-factoring 
S-rule could potentially misapply to verbs like present. 
The surprise example shows how the addition of informa- 
tion about semantic class membership to verb entries allows the 
system to handle a particular phenomenon (or lexieal property) 
common to all verbs in a particular class, with the help of a sin- 
gle S-rule. We refer to this approach as the verb class approach. 
We could have achieved the same effect in another way: in- 
stead of specifying verb class membership in the entry of each 
verb, we could have explicitly registered the lexical properties 
that apply to the verb (or the names of the corresponding S- 
rules). Taking this approach, the lexical entry for surprise 
would indicate property-factoring instead of emotionabreaction 
but would otherwise be unchanged. 
(52) (surprise :verb :property-factorlng) 
This approach could allow us to dispense with the PROVIDED 
clause in the S-rules since the lexical entry of a verb would be 
tagged with the set of S-rules that could apply to that verb. 
However, the verb class approach has a clear advantage over 
the alternative lexical property approach when more than one 
property is involved. Typically each semantic class of verbs has 
a number of properties associated with it, which must ultimately 
be handled by a natural language system. 
If we take the lexical property approach, whenever we add 
a new lexical property, we would need to write an S-rule for 
this new property, and we would then have to add the property 
to the lexical entry of each member of the class of verbs that 
exhibit this property. 
In contrast, if we take the verb class approach, it is easy to 
extend the system to handle new properties of a class of verbs. 
All that is required is the addition of the appropriate S-rule, 
formulated so that it triggers on this class of verbs. There is no 
need to alter the lexical entries of the members of the class in 
any way if the lexical entry of each verb in the class indicates 
that it is a member of this class. Thus the verb class approach, 
unlike the lexical property approach, allows a more modular 
system design; this in turn allows the coverage of the system to 
be extended more easily. 
320 
To illustrate that each class of verbs is characterized by a 
whole cluster of properties, we survey just a few of the properties 
associated with the emotional-reaction verbs. They participate 
in several transitivity alternations--the middle alternation in 
(53) and (54) (see Keyser and Roeper \[1984\], Hale and Keyser 
\[1987\], among others) and the null-object alternation in (55) and 
(56) (aizzi \[1986\]): 
(53) Dogs frighten little children. 
(54) Little children frighten easily. 
(55) Thunder never fails to frighten people. 
(56) Thunder never fails to frighten. 
The subject of the middle use (54) bears the same semantic 
relation to the verb as the object of the transitive use (53), 
while the subject of the nnll-object use (56) bears the same 
semantic relation to the verb as the subject of the transitive 
use (55). The null-object alternation, like the indefinite object 
alternation, involves an unexpressed but understood object in 
the intransitive variant. However, the understood object here is 
interpreted as "people". 
Emotional-reacti0n verbs are also set apart by another prop-- 
erty: they only have passive nominals. Thus, the children's dis- 
appointment means that the children experienced disappoint- 
ment (passive interpretation), not that they caused disappoint- 
ment (active interpretation). Compare the interpretation of this 
nominal to that of the nominal related to a verb like describe: 
Gene's description can refer to the description that Gene gave 
(active interpretation) or to a description that someone gave of 
Gene (passive interpretation). 
We return to the example involving present discussed in the 
previous section. The alternation manifested by the verb present 
is exhibited by the members of a small class of verbs which also 
includes entrust, furnish, supply, and trust. The Present S-rule 
in (31) can be generalized so that it will apply to the entire class 
of verbs like present, which we name the present class. 
(57) Presentation S-rule 
If <<subject verb objectl> with object2> 
Then <<subject verb object2> to objectl> 
Provided verb E present class 
Now suppose that after typing (58) we ask question (59): 
(58) Mark supplied the restaurant with fresh vegetables. 
(59) Did Mark supply fresh vegetables to the restaurant? 
Since the verb supply is a member of the Present class, the SLrule 
in (57) applies and START will be able to answer the question 
correctly: 
(60) Yes, Mark supplied fresh vegetables to the restaurant. 
The examples described in this section show how the trans- 
parent syntax of the S-rules coupled with the information about 
verb class membership provided by the Lexicon facilitates fluent 
and flexible dialog between the user and the language processing 
system. 
6. Employing S-Rules: Additional Examples 
In this section we present additional dialogues that START can 
handle. These interchanges are chosen to illustrate the use of 
S-rules as well as the range of coverage of the START system. 
In the process we introduce S-rules that handle several more 
semantic-syntactic interdependencies, including the indeflrfite 
object and reflexive alternations discussed in the introduction 
to the paper. 
At the beginning of tim paper we presentcd two short in- 
terchange.,~ between two speakers of English and asked what it 
would take for a natural language system to be able to partici- 
pate in these interchanges. We repeat the examples below: 
(61) Speat:er 1: Sally ate a pear. 
Speal~:er 1: Did Sally eat? 
Speat:er 2: Yes. 
(62) 5peat:er 1: David dressed the baby. 
Speaker 1: Did David dress? 
Speaker 2: I don't know. 
These examples were used to ilhlstrate some of the semantic- 
syntactic ;nterdependencies characteristic of verbs that partio 
ipate in bansitivity alternations. Verbs that have both transi- 
tive and i*ttransitive uses differ in the interpretations associated 
with their intransit, ive uses, as these interchanges were intended 
to show. S-rules allow START to answer questions that draw 
on knowledge of the semantic-syntactic interdependencies that 
are manitk~sted in transitivity alternations. 
The two transitivity alternations relevant to the interchan- 
ges above are the indefinite object alternation (with eat) and 
the reflexive alternation (with dress). When used intransitively, 
verbs thai. participate in the indefinite object alternation de- 
scribe actions where there is an iml)lied object which is under- 
stood to be something that is a typical object of the verb. Thus: 
(63) Sally ate a pear. ==> Sally ate. (i.e., Sally ate some food 
or a meal.) 
We can formulate an S-rule that makes explicit the relationship 
between the transitive and the intransitive use of such verbs. 
(64) indefinite-object S-ruh: 
If <subject verb object> 
"1?he a <subject verb> 
Provided verb E activi~y/oec.ll, patio.a chess 
Now if S':CAt~'F were asked "Did Sally eat?" after having been 
told that Sally ate a pear, START would be able to answer 
"Yes", assunfing that ear's lexical entry indicates that it is a 
member st the activity/occupation class. 
In order to handle the dress interchange, START needs an 
S-rule that captures the properties of the reflexive alternation 
shown by verbs of grooming. The intransitive use of a verb 
that participates in this alternation implies that the subject 
perfornmd tl,e action denoted by the verb on himself or herself. 
(65) David dressed. --==> David dressed himself. 
We can capture the special interpretation associated with the 
intransitiw~ use of a verb like dress by means of an S-rule: 
(66) Reflexive S J'ule 
If <subject verb> 
Then <subject verb subject> 
_Provided verb E grooming class 
Now if asked "Did ~leff dress himself?", after being told that Jeff 
dresscd, tim system would answer "Yes" since the lexical entry 
of dress in(iicates that it is a verb of grooming. START will also 
be ablc to handle the following more complex interchange: 
(67) Input: Ann dressed and Mau'y dressed the baby. 
Question: Who dressed herself? 
STA.II!I': Ann dressed herself. 
Quest~ion: Who dressed the baby? 
START: Mary dressed the baby. 
Oueation: Did Mary dress? 
STAt/T: I don't know. 
In a more complex situation, S-rules are allowed to trigger 
each other and to ask each other for help. To exemplify this, we 
introduce two more S-rules. These rules are used to capture the 
properties of a cl~s of verbs that has received substantial at- 
tention in the linguistics literature (see Anderson \[1971\], Fraser 
\[19711, Schwarz-Norman \[1976\], Jeffries and Willis \[1984\], Rap- 
paport and Levin \[1986\], among othms). We refer to this class as 
tile spray/load class after two prototypicM members. The verbs 
in this class describe actions that involve putting substances or 
materials on surfaces (e.g. spray) or in containers (e.g. load). 
What distinguishes one member of the spray/load class from 
another is the manner in which this action is performed. Some 
members of this class are listed below: 
(68) load, pack, pile, smear, spray, spread, stack, stuff, wrap, ... 
All the members of this class display an argument alternation, 
the locative alternation; they arc." found in two syntactic frames, 
as illustrated below: 
(69) Miriam sprayed paint on tl~e wall. (locative variant) 
(70) Miriam sprayed the wall with paint. (with variant) 
(71) Jan loaded hay on the truck. 
(72) ,Jan loaded the truck with hay. 
Although the sentences in each of these pairs might appear at 
first glance to be paraphrases, they are not. For instance, in the 
first pMr, only (69), the locative w~riant, may be used to describe 
a situation where a small portion of the. wall is covered wifll 
paint as a result of the spraying (the partitive interpretation), 
while sentence (70), the with variant, necessarily implies ~hat, 
the wall is entirely covered with paint as a result of the action 
(the holistic interpretation). The sentences in each pair ~'~rc near 
paraphrases in the sense that the truth of the with variant entails 
the truth of the locative varimlt, but not vice versa. 
Before we can ibrmulate an S-rule that captures ~;lle relation 
between the two variants, we need to look more closely at the 
locative variant. When used in the locative variant, apray/load 
verbs are found with a wide range of locative prepo.,;itions (tlm 
set of prepositions indicating spatial relationships). 
(73) Miriam sprayed paint on/under/around the table. 
When the .with variant of a spray/load verb is part,phrased by 
the locative variant, typically only one d the range of locative 
prepositions is possible; the choice is a function of the verb in- 
volved. The verbs stray and load both involve the preposition 
on in the locative variant, but the w.wb stuff requires the prepo.- 
sition into, while the verb wrap requires around: 
(74) Jessiea stuffed the pillow with feattmrs. 
(75) aessica stuffed the feathers into tile pillow. 
(76) Oabriella wrapped tile package with paper. 
(77) Gabriella wrapped paper around the package. 
It appears that each spray~load verb is associated with a default 
locative preposition that must be indicated in its lexical entry: 
(78) (spray :verb :spray-load :defimlt.-preposition on) 
(79) (wrap :verb :spray-load :default-preposition around) 
(80) (stuff :verb :spray-load :default-preposition into) 
We can now write an S-rule that captures the relation be 
tween the with and locative variants of the locative alternation: 
(8:1.) Itolistie/pariitive S-rule 
If <<subject verb object1> with object2> 
Then <<subject verb object2> prepLoc object1> 
Provided verb (? spray/load class 
In this S-rule tile variable ~prepLoc' is instantiated with the 
default locative preposition associated with tile verb tim rule is 
applying to. 
521 
Using the Holistic/partitive S-rule, START is easily able to 
handle the following interchanges: 
(82) Input: Matilda stuffed the suitcase with books. 
Question: Did Matilda stuff the books into the suitcase? 
START: Yes, Matilda stuffed the books into the suitcase. 
(83) Input: Miriam wrapped the book with paper. 
Question: Who wrapped the paper around the book? 
START: Miriam wrapped the paper around the book. 
To illustrate how S-rules can trigger each other, we in- 
troduce an S-rule motivated by another property of spray~load 
verbs. We have seen that the with variant of a spray~load verb 
entails the locative variant. In addition, the locative variant, 
which describes the placement of some substance in a container 
or on some surface, implies that the substance will be in the con- 
taincr or on the surface. That is, sentence (84) entails sentence 
(85): 
(84) Jan loaded hay on the truck. 
(85) Hay was on the truck. 
The following S-rule can be used to capture this property of 
spray~load verbs. 5 
(86) Resulting Location S-rule 
If <<subject verb object1> prePLOC object2> 
Then <<subject be> prepLoc object2> 
Provided verb E spray/load class 
We attribute the entailment relationship between the two vari- 
ants described by the Holistic/partitive S-rule to a speaker's lin- 
guistic knowledge, while the entailment relationship described 
by the Resulting Location S-rule reflects real world knowledge. 
We introduced the Resulting Location S-rule in order to show 
how one S-rule can operate on the output of another. The Holis- 
tic/partitive S-rule and the Resulting Location S-rule together 
allow sentence (88) to be deduced fl'om sentence (87): 
(87) ,\]an loaded the truck with hay. 
(88) Hay was on the truck. 
By using both these S-rules, the system can handle the following 
interchange: 
(89) Input: Jan loaded the truck with hay. 
Question: Was there hay on the truck? 
START: Yes, there was hay on the truck. 
The syntactic component of START is able to undo the effects 
of the There-Insertion rule in the question, translating it into 
the T-expression 
(90) <<hay be> on truck> 
This T-expression does not match anything in the knowledge 
base, so START tries to apply S-rules. The Resulting Loca- 
tion S-rule used in the backward mode can apply to this T- 
expression, suggesting that START then searches for the T- 
expression that matches template (91) below, where the variable 
verb is restricted to members of the spray/load class. 
(91) <<subject verb hay> on truck> 
This T-expression also does not match against anything in the 
knowledge base, but it triggers the Holistic/partitive S-rule, 
5The PROVIDED clause in the Resulting Location S-rule restricts 
this rule to the sprayfload verbs. Actually this S-rule applies to a 
larger set of verbs, the set of causative verbs of change of location, 
including put, place, insert and transitive move, slide, drop, as well 
as the sprayfload verbs. For instance, if Rebecca put the books 
on the table, then the books are on the table. We have given 
a restrictive formulation of the PROVIDED clause in this S-rule 
since we do not want to discuss the implications of introducing a 
hierarchy of verb class types for the operation of S-rules. 
322 
which when applied produces 
(92) <<subject verb truck> with hay> 
Finally, T-expression (92) matches with the T-expression 
(93) <<Jan load truck> with hay> 
obtained from the Input sentence in (89), allowing START to 
generate the appropriate answer. 
Below we show some further examples of interchanges suc- 
cessfully handled by START through the use of additional S- 
rules. These examples involve verbs of manner of motion, listed 
in (94), and verbs of creation, listed in (97): 
(94) climb, cross, fly, gallop, jump, march, swim, walk, ... 
(95) Input: Albert jmnped over the fence. 
Question: Did he jump the fence? 
START: Yes, Albert jumped the fence. 
(96) Input: Candy climbed up the mountain. 
Question: What did Candy climb? 
START: Candy climbed the mountain. 
(97) bake, carve, croche~ hammer, knit, sew, weave, ... 
(98) Input: Jessica baked the dough into round loaves. 
Question: What did Jessica bake round loaves from? 
START: Jessica baked round loaves from ~he dough. 
(99) Input: Miriam carved wood into a doll. 
Question: Out of what did Miriam carve the doll? 
START: Miriam carved the doll out of the wood. 
At any given moment many S-rules may be hidden in the 
computer's memory examining the output flow generated by 
START and waiting tbr their turn to participate in the deduc- 
tion process. S-rules fundamentally expand the power of our 
system; they open a window into the intricate world of semantic-. 
syntactic interactions. 
7. Lexical Acquisition 
A natural language system must be built in such a way that 
is is easy to expande its coverage, not only by increasing the 
size of the lexicon but also by adding to the set of different 
phenomena covered by its grammar. Due to the large number 
of semantic-syntactic interdependencies, increasing the coverage 
of a system's grammar might seem to be prohibitively expensive. 
It would require meddling with the entries of every verb in the 
Lexicon, in order to register its behavior with respect to the new 
phenomenon. But once a phenomenon is assoeiated with one or 
more verb classes, it need only be associated with these classes. 
There is no need to tamper with the entries of the individual 
verbs or construct verb-specific S-rules, as long as the lexical 
entries of verbs indicate class membership. Thus the problem 
of incorporating new phenomena is considerably simplified. 
The process of lexical acquisition (adding new words to the 
Lexicon and specifying the relevant information about them) i,-~ 
very simple in START. Introducing a new lexical item amounts 
to little more than appending it to a list of similar words, adding 
a few idiosyncratic features when necessary. For example, if we 
wanted to add the verb annoy to START's lexicon, we would 
simply have to add the verb together with an indication that it 
is a member of the emotional-reaction class. 
(1O0) (annoy :verb :emotional-reactlon) 
The lexical entry would not need to contain an explicit indi- 
cation that this verb participates in the property-factoring al- 
ternation since the S-rule representing this alternation makes 
explicit that this property holds of all members of the emotional- 
reaction clw~s. The class mcntbershiI) indication in annoy's lex- 
ical entry would allow the S-rules that apply to the emotional- 
reaction cla:~s in general to apply to this verb in partienlar, so 
that STAI~C will be able t(, hm~(lle sequences such as the fol- 
lowing: 
(10\]) Inpu*: '_/'he dog annoyed the guests with its loud barking. 
(2ueaiion: Whom did the dog's lond harking annoy? 
S!/'A R.T: q?he dog's loud barking annoyed the guests. 
The acquisition of S-rules is equally simple in the STAHT 
system due to a special component ~ha.t allows STAI-~I' to in- 
~eer S..rules tram examples. Adding a new S-rule to the system 
requires typing in a set of English sentences which capture a 
specific insta,me of the rule. lh)r insl;ance, a pair of declar- 
~tive seats, tees (sa,:h as (35) and (36)), which exemplify the 
property-f~w.toring alternation, can be used by S'\]'ARSi? to i~ffer 
the celated S rule. '.I.'o do t, his, STAtG.' analyzes the sentences, 
queries th,~ user for ad(titional information regarding elements 
of co~responding T-expre'~sions (a,';certaining whether they are 
too.lashing variable, Z constants, or predicates), and then builds 
trod general\zes the S--rule automatically. 
Carefld examination of English verb cbmses (see Levin \[to 
appear\]) combined with the effective employment of S-rules al- 
lows the system to red*me to a minimuln the amount of idiosyn- 
coat, is ,~ynt~etie and semantic information in the Lexicon. All 
this makes t,he system transportable; that is, it is easily adapt- 
able to new domains. 
go Coneiasion 
The addition of a componen~ that explicitly encodes verb classes 
and their characteristic properties, enables the START system 
to handle s wide range of phenomena reflecting sernantic-syn- 
t~etlc correspondences that are characteristic of English verbs. 
By t?~(:toring properties that, belong to whole classes of verl:,s out 
of the entries of individual verbs and letting these entries simply 
designate the verb's class membership, we do more than merely 
simplify entries. We facilitate the addition of new words to the 
lexicon and make it easier to extend tile system's coverage of 
linguistic phenomena. 
A(.'knowledgn-mnd;s 
We arc gral;e.ful to aa~e Simpson, Misha Katz, and Tom Maril\] 
for bdpfnl comme_u{s mtd suggestions concerning this paper, and 
to Jeff Pah,mcci who rontributed signiiieantly to the Question- 
Answering pa~rt of the system. 
This paper describes research done at the Massachusetts 
Institute of Technology. Support tbr i(atz's work was provided 
in part by the Advanced P~esearch Projects Agency under Of- 
flee of Naval Resc~rch contract N0014-85-K-0124. Support for 
Levin~s work w~m provided in part by a grant to the Lexicon 
Project of the MIT Center for Cognitive Science from the Sys- 
tem Derek,parent Foundation. 

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