On the Acquisition of Lexical Entries: 
The Perceptual Origin of Thematic Relations 
James Pustejovsky 
Department of Computer Science 
Brandeis University 
Waltham, MA 02254 
617-736-2709 
jamesp~br andeis.csnet-relay 
Abstract 
This paper describes a computational model of concept 
acquisition for natural language. We develop a theory 
of lexical semantics, the Eztended Aspect Calculus, which 
together with a ~maxkedness theory" for thematic rela- 
tions, constrains what a possible word meaning can be. 
This is based on the supposition that predicates from the 
perceptual domain axe the primitives for more abstract 
relations. We then describe an implementation of this 
model, TULLY, which mirrors the stages of lexical acqui- 
sition for children. 
I. Introduction 
In this paper we describe a computational model of con- 
cept acquisition for natural language making use of po- 
sitive-only data, modelled on a theory of lexical seman- 
tics. This theory, the Eztende~t Aspect Calculus acts to- 
gether with a maxkedness theory for thematic roles to 
constrain what a possible word type is, just as a gram- 
mar defines what a well-formed tree structure is in syntax. 
We argue that linguistic specific knowledge and learning 
principles are needed for concept acquisition from positive 
evidence alone: Furthermore, this model posits a close in- 
teraction between the predicates of visual perception and 
the early semantic interpretation of thematic roles as used 
in linguistic expressions. In fact, we claim that these re- 
lations act as constraints to the development of predicate 
hierachies in language acquisition. Finally, we describe 
TULLY, an implementation of this model in ZETALXSP 
and discuss its design in the context of machine learning 
research. 
There has been little work on the acquisition of 
thematic relation and case roles, due to the absence of 
any consensus on their formal properties. In this research 
we begin to address what a theory of thematic relations 
might look like, using learnabUity theory as a metric for 
evaluating the model. We claim that there is an impor- 
tant relationship between visual or imagistic perception 
and the development of thematic relations in linguistic us- 
age for a child. This has been argued recently by Jackend- 
off (1983, 1985) and was an assumption in the pioneering 
work of Miller and Johnson-Laird (1976). Here we argue 
that the conceptual abstraction of thematic information 
does not develop arbitrarily but along a given, predictable 
path; namely, a developmental path that starts with tan- 
gible perceptual predicates (e.g. spatial, causative) to 
later form the more abstract mental and cognitive predi- 
cates. In this view thematic relations are actually sets of 
thematic properties, related by a partial ordering. This 
effectively establishes a maxkedness theory for thematic 
roles that a learning system must adhere to in the acqui- 
sition of lexical entries for a larlguage. 
We will discuss two computational methods for 
concept development in natural language: 
(1) F~ature Relaxation of particular features of the ar- 
guments to a verb. This is performed by a con- 
straint propagation method. 
(2) Thematic Decoupling of semantically incorporated 
information from the verb. 
When these two learning techniques are combined with 
the model of lexical semantics adopted here, the stages 
of development for verb acquisition are similar to those 
acknowledged for child language acquisition. 
2. Learnabillty Theory and Concept De- 
velopment 
Work in machine learning has shown the useful- 
ness to an inductive concept-learning system of inducing 
"bias" in the learning process (cf. \[Mitchell 1977, 1978\], 
\[Michalski 1983\]). An even more promising development 
is the move to base the bias on domain-intensive models, 
as seen in \[Mitchell et al. 1985\], \[Utgoff 1985\], and \[Win- 
ston et al. 1983 I. This is an important direction for those 
concerned with natural language acquisition, as it con- 
verges with a long-held belief of many psychologists and 
linguists that domain-specific information is necessary for 
learning (cf. \[Slobin 1982\], \[Pinker 1984\], {Bowerman 
1974\], \[Chomsky 1980\]). Indeed, Berwick (1984) moves in 
exactly this direction. Berwick describes a model for the 
acquisition of syntactic knowledge based on a restricted 
X-syntactic parser, a modification of the Marcus parser 
(\[Marcus 1980\]). The domain knowledge specified to the 
system in this case is a parametric parser and learning 
system that adapts to a particular linguistic environment, 
given only positive data. This is just the sort of biasing 
necessary to account for data on syntactic acquisition. 
172 
One area of language acquisition that has not been 
sufficiently addressed within computational models is the 
acquisition of conceptual structure. For language acquisi- 
tion, the problem can be stated as follows: How does the 
child identify a particular thematic role with a specific 
grammatical function in the sentence? This is the prob- 
lem of mapping the semantic functions of a proposition 
into specified syntactic positions in a sentence. 
Pinker (1984) makes an interesting suggestion (due 
originally to D. Lebeaux) in answer to this question. He 
proposes that one of the strategies available to the lan- 
guage learner involves a sort of ~template matching" of 
argument to syntactic position. There are canonical con- 
j~gurat{orts that are the default mappings and non-cano- 
nicoJ mappings for the exceptions. For example, the tem- 
plate consists of two rows, one of thematic roles, and the 
other of syntactic positions. A canonical mapping exists 
if no lines joining the two rows cross. Figure 1 shows a 
canonical mapping representing the sentence in (1), while 
Figure 2 illustrates a noncanonical mapping representing 
sentence (2). 
0-roles: ~~L 
Syntactic roles: SUBJ OBJ OBL 
Figure 1 
e-roles: A Th G/S/L 
Syntactic ro~O BL 
Figure 2 
(1) Mary hit Bill. 
(2) Bill was hit by Mary. 
With this principle we can represent the productivity of 
verb forms that are used but not heard by the child. We 
will adopt a modified version of the canonical mapping 
strategy for our system, and embed it within a theory of 
how perceptual primitives help derive linguistic concepts. 
As mentioned, one of the motivations for adopt- 
ing the canonical mapping principle is the power it gives 
a learning system in the face of positive-only data. In 
terms of learnability theory, Berwick (1985) (following 
\[Angluin 1978\]) notes that to ensure successful acquisi- 
tion of the language after a finite number of positive ex- 
amples, something llke the Subset Principle is necessary. 
We can compare this principle to a Version Space model 
of inductive learning( \[Mitchell 1977, 1978\]), with no neg- 
ative instances. Generalization proceeds in a conservative 
fashion, taking only the narrowest concept that covers the 
data. 
How does this principle relate to lexical seman- 
tics and the way thematic relations are mapped to syn- 
tactic positions? We claim that the connection is very 
direct. Concept learning begins with spatial, temporal, 
and causal predicates being the most salient. This follows 
from our supposition that these are innate structures, or 
are learned very early. Following Miller and Johnson- 
Laird (1976), \[Miller 1985\], and most psychologists, we 
assume the prelinguistic child is already able to discern 
spatial orientations, causation, and temporal dependen- 
cies. We take this as a point of departure for our theory 
of markedness, which is developed in the next section. 
3.0 Theoretical Assumptions 
3.1 The Extended Aspect Calculus 
In this section we outline the semantic framework 
which defines our domain for lexical acquisition. In the 
current linguistic literature on case roles or thematic re- 
lations, there is little discussion on what logical connec- 
tion exists between one e-role and another. Besides being 
the workhorse for motivating several principles of syn- 
tax (cf. \[Chomsky 1981\], \[Willi~ms 1980\]) the most that 
is claimed is that Universal Grammar specifies a reper- 
toire of thematic relations (or case roles), Agent, Theme, 
Patient, Goal, Source, Instrument, and that every NP 
must carry one and only one role. It should be remem- 
bered, however, that thematic relations were originally 
conceived in terms of the argument positions of seman- 
tic predicates such as CAUSE and DO. * That is a verb 
didn't simply have a list of labelled arguments 2 such as 
Agent and Patient, but had an interpretation in terms of 
more primitive predicates where the notions Agent and 
Patient were defined. The causer of an event (following 
Jackendoff (1976)) is defined as an Agent, for example, 
c ,4u s E(=, ,) -. Ag,.~(=). 
Similarly, the first argument position of the pred- 
icate GO is interpreted as Theme, as in GO(=,y,z). The 
second argument here is the SOURCE and the third is 
called the GOAL. 
The model we have in mind acts to constrain the 
space of possible word meanings. In this sense it is similar 
to Dowty's aspect calculus but goes beyond it in embed- 
ding his model within a markedness theory for thematic 
types. Our model is a first-order logic that employs sym- 
bols acting as special operators over the standard logical 
vocabulary. These are taken from three distinct semantic 
fields. They are: causal, spatial, and aspectual. 
The predicates associated with the causal field are 
Cau~e, (C,), C~se~ (C2), and l.stru,ne.t (I). The spatial 
field has only one predicate, Locatiue, which is predicated 
of an object we term the Th~me. Finally, the aspectual 
i CfiJackendoff (1972, 1976) for a detailed elaboration of 
this theory. 
2 This is now roughly the common assumption in GB, 
GPSG, and LFG. 
173 
field has three predicates, representing the three temporal 
intervals t~, beginning, t2, middle, and t3, end. From the 
interaction of these predicates all thematic types can be 
derived. We call the lexical specification for this aspectual 
and thematic information the Thematic Mapping Indez. 
As an example of how these components work to- 
gether to define a thematic type, consider first the dis- 
tinction between a state, an activity (or process), and an 
accomplishment. A state can be thought of as reference 
to an unbounded interval, which we will simply call t2; 
that is, the state spans this interval. 3 An activity or pro- 
tess can be thought of as referring to a designated initial 
point and the ensuing process; in other words, the situa- 
tion spans the two intervals tt and t2. Finally, an event 
can be viewed as referring to both an activity and a des- 
ignated terminating interval; that is, the event spans all 
three intervals, it, t2, and is, 
Now consider how these bindings interact with the 
other semantic fields for the verb run in sentence (8) and 
give in sentence (9). 
(8) John ran yesterday. 
(9) John gave the book to Mary. 
We associate with the verb run an argument structure of 
simply rim(=}. For give we associate the argument struc- 
ture ~v,(=, v, =). The Thematic Mapping Index for each is 
given below in (10) and (11). 00) 
L/!, 
(11) 
Th t ,!) 
tt t 2 
The sentence in (8) represents a process with no logical 
culmination, and the one argument is linked to the named 
case role, Theme. The entire process is associated with 
both the initial interval t~ and the middle interval t2. The 
argument = is linked to C~ as well, indicating that it is 
an Actor as well as a moving object (i.e. Theme). This 
represents one TMI for an activity verb. 
The structure in (9) specifies that the meaning of 
give carries with it the supposition that there is a logical 
This is a simplication of our model, but for our 
purposes the difference is moot. A state is actually inter- 
preted as a primitive homogeneous event-sequence, with 
downward closure. Cf. \[Pustejovsky, 1987\], 
4 \[Jacl~endoff tOSS\] develops a similar idea, but vide in/ra for discussion. 
culmination to the process of giving. This is captured by 
reference to the final subinterval, is. The linking between 
= and the L associated with tt is interpreted as Source, 
while the other linked arguments, y and z are Theme (the 
book) and Goa/, respectively. Furthermore, = is specified 
as a Causer and the object which is marked Theme is also 
an affected object (i.e. Patient). This will be one of the 
TMIs for an accomplishment. 
In these examples the three subsystems are shown 
as rows, and the configuration given is lexically specified. 
4 
3.2 A Markedness Theory for Thematic Roles 
As mentioned above, the theory we are outlining 
here is grounded on the supposition that all relations in 
the language are suffiently described in terms of causal, 
spatial and aspectual predicates. A thematic role in this 
view is seen as a set of primitive properties relating to the 
predicates mentioned above. The relationship between 
these thematic roles is a partial ordering over the sets of 
properties defining them. It is this partial ordering that 
allows us to define a markedness theory for thematic roles. 
Why is this important? 
If thematic roles are assigned randomly to a verb, 
then one would expect that there exist verbs that have 
only Patient or Instrument, or two Agents or Themes, for 
example. Yet this is not what we find. What appears to 
be the case is that thematic roles are not assigned to a 
verb independently of one another, but rather that some 
thematic roles are fixed only after other roles have been 
established. For example, a verb will not be assigned a 
GOAL if there is not a THEME assigned first. Similarly, 
a LOCATIVE is dependent on there being a THEME 
present. This dependency can be viewed as an acquisition 
strategy for learning the thematic relations of a verb. 
Now let us outline the theory. We begin by estab- 
lishing the most unmarked relation that an argument can 
bear to its predicate. Let us call this role Them,~. The 
only semantic information this carries is that of an exis- 
tential quantifier. It is the only named role outside of the 
three interpretive systems defined above. Normally, we 
think of Them, as an object in motion. This is only half 
correct, however, since statives carry a Theme readings as 
well. It is in fact the feature \[±motion\] that distinguishes 
the role of Mary in (1) and (2) below. 
(1) Stative: l-motion I Mary sleeps. 
(2) Active: \[+motion\] Mary fell. 
This gives us our first markedness convention: 
(3) Therr=ee--Theme.~/\[+motion\] 
(3) Themery-..Themes/\[-motior=\] 
174 
where ThemeA is an "activity" Theme, and Themes is a 
stative. 
Within the spatial subsystem, there is one variable 
type, Location, and a finite set of them L1, L~... L~. The 
most unmarked location is that carrying no specific aspec- 
tual binding. That is, the named variables are Ls and Lz 
and are commonly referred to as Source and Goal. Thus, 
Lu is the unmarked role. The limitations on named loca- 
tive variables is perhaps constrained only by the aspectual 
system of the language (rich aspectual distinction, then 
more named locative variables). The markedness conven- 
tions here are: 
(4) Lu -* S/B 
(s) L~ -- C/E 
Within the causal subsystem there are three pred- 
icates, Cl, C2, and I. We call C2, (the traditional Patient 
role) is less marked than c~, but is more marked than I. 
These conventions give us the core of the primitive 
semantic relations. To be able to perform predicate gen- 
eralization over each relation, however, we define a set of 
features that applies to each argument within the seman- 
tic subsystems. These are the abstraction operators that 
allow a perceptual-based semantics to generalize to non- 
perceptual relations. These features also have marked 
and unmarked values, as we will show below. There are 
four features that contribute to the generalization process 
in concept acquisition: 
(a) l±~b,tra,t\] (b) \[+d~r,~t\] 
(c) \[±,o,.pl,t,\] (d) \[±.~i~t,\] 
The first feature, abttract, distinguishes tangible 
objects from intangible ones. Direct will allow a gradi- 
ence in the notion of causation and motion. The third 
feature, cornplete, picks out the extension of an argument 
as either an entire object or only part of it. Ani~v~ac~l has 
the standard semantics of labeling an object as alive or 
not. 
Let us illustrate how these operators abstract over 
primitive thematic roles. By changing the value of a fea- 
ture, we can alter the description, and hence, the set of 
objects in its extension. Assume, for example, that the 
predicate C1 has as its unmarked value, \[+Direct\]. 
(6) C,\[UDir,,tl --\[+Vir,ctl 
By changing the value of this feature we allow CI, the 
direct agent of an event, to refer to an indirect causer. 
(7) Ae,.t\[+D~rect I <@ Aee,~tl-Dir,ct \] 
Similarly, we can change the value of the default setting 
for the feature I+Complet~\] to refer to a subcausation (or 
causation by part). 
(8) Agent{+CompleU\] <~ Agent\[-CompleteJ 
These changes define a new concept, "effector', which is 
a superset of the previous concepts given in the system. 
The same can be done with C'~ to arrive at the concept of 
an "effected object." We see the difference in interpreta- 
tion in the sentences below. 
a. John intentionally broke the chair. (Agent-direct) 
b. John accidentally broke that chair when he sat 
down. (Agent-indirect) 
c. John broke the chair when he fell. (Effector) 
Given the manner in which the features of primi- 
tive thematic roles are able to change their values, we are 
defining a predictable generalization path that relations 
incorporating these roles will take. In other words, two 
concepts may be related thematically, but may have very 
different extensional properties. For example, give and 
take are clearly definable perceptual transfer relations. 
But given the abstractions available from our marked- 
ness theory, they are thematically related to something 
as distant as "experiencer verbs", e.g. please, as in "The 
book pleased John." This relation is a transfer verb with 
an incorporated Theme; namely, the "pleasure." s 
If we apply these features in the spatial subsystem, 
we can arrive at generalized notions of location, as well 
as abstracted interpretations for Theme, Goal and Source. 
For example, given the thematic role Th - A with the fea- 
ture \[-Abstract\] in the default setting, we can generalize 
to allow for abstract relations such as like, where the ob- 
ject is not affected, but is an abstract Theme. Similarly, 
the Theme in a sentence such as (a) can be concrete and 
direct, or abstract, as in (b). 
(a) have(L, rh) Mary has a book. 
(b) have(L, Yh) Mary has a problem with Bill. 
In conclusion, we can give the following dependencies be- 
tween thematic roles: 
{r~eme} 
{~} {s, c} 
{c,} 
s Cf. Pustejovsky (1987) for an explanation of this term 
and a full discussion of the extended aspect calculus. 
175 
The generaliztion features apply to this structure to build 
hierarchical structures (Cf. {Keil 1979\], \[Kodratoff 1986\]). 
This partial ordering allows us to define a notion of cov- 
crs'ng, as with a semi-lattice, from which a strong princi- 
ple of functional uniqueness is derivable (of. \[Jackendoff 
1985\]). The mapping of a thematic role to an argument 
follows the following principle: 
(9) Maximal Assignment Principle An argument 
will receive the maximal interpretation consistent 
with the data. 
This says two things. First, it says that an Agent, for 
example, will always have a location and theme role as- 
sociated with it. Furthermore, an Agent may be affected 
by its action, and hence be a Patient as well. Secondly, 
this principle says that although an argument may bear 
many thematic roles, the grammar picks out that function 
which is mazimall!; specific in its interpretation, accord- 
ing to the markedness theory. Thus, the two arguments 
might be Themes in "John chased Mary", but the the- 
matic roles which maximally characterize their functions 
in the sentence are A and P, respectively. 
4. The Learning Component 
4.1 The Form of the Input 
The input is a data structure pair; an event se- 
quence expression and a sentence describing the event. 
The event-sequence is a simulated output from a middle- 
level vision system where motion detection from the low- 
level input has already been associated with particular 
object types. 6 
The event-sequence consists of three instantaneous 
descriptions (IDa) of a situation represented as intervals. 
These correspond to the intervals t~, t2, and ts in the 
aspect calculus. The predicates are perceptual primi- 
tives, such as those described in Miller and Johnson- 
Laird (1976) and Maddox and Pustejovsky (1987), such 
as \[Ar(t~, ~) ~ ~ = \[O,V(,,, d t, ,4,,,,,.~t,(,,) ~, Mo,,,~(~,) ~, ...\]\]. 
The second object is a linguistic expression (i.e. a sen- 
tence), parsed by a simple finite state transducer. ~ 
s For a detailed discussion of how the visual processing 
and linguistic systems interact, cf. Maddox and Pustejovsky 
(1987). 
We are not addressing any complex interaction between 
syntactic and semantic acquisition in this system. Ideally, we 
would like to integrate the concept acquisition mechanisms here 
with a parser such as Berwick's, Cf. Berwick 1985. 
4.2 The Acquisition Procedure 
We now turn to the design of the learning program 
itself. TULLY can be characterized as a domain-intensive 
inductive learning system, where the generalizations pos- 
sible in the system are restricted by the architecture im- 
posed by the semantic model. We can separate clearly 
what is given from what is learned in the system, as shown 
in Figure 1. 
GIVEN 
Extended Aspect Calculus 
0-Markedness Theory 
Canonical Mapping 
Rule Execution Loop 
ACQUIRED 
Verbal Lexical semantics 
Argument-function mapping 
Predication Hierarchy 
Figure 1 
In order to better understand the learning mecha- 
nism, we will step through an example run of the system. 
First, however, we will give the rule execution loop which 
the system follows. 
Rule Execution Loop 
1. Instantiate Existing Thematic Indexes 
INSTANTIATE: Attempt to do a semantic analy- 
sis of word given using existing Thematic Mapping 
Indexes. If the analysis fails then go to 2. 
2. Concept.acquisition phase. 
Note failure: Credit assignment. 
Link arguments to roles according to Canonical 
Mapping. 
3. Build new Thematic Mapping Index 
LINK and SHIFT: Constructs new index accord- 
ing to the Extended Aspect Calculus using infor- 
mation from credit assignment in (2). If this fails 
then go to (4). 
4. Invoke Noncanonical Mapping Principle. 
If (3) fails to build a mapping for the lexical item in 
the input, then the rule INTERSECT is invoked. 
This allows the lines to cross from any of the in- 
terpretive levels to the argument tier. 
5. Generalization Step. 
This is where the markedness theory is invoked. 
Induction follows the restrictions in the theory, 
where generalization is limited to one of the stated 
types. 
176 
Assume that the first input to the system is the 
sentence ~Mary hit the cat," with its accompanying event 
sequence expression, represented as a situation calculus 
expression. INSTANTIATE attempts to map an exist- 
ing Thematic Mapping \[ndez onto the input, but fails. 
Stage (2) is entered by the failure of (1), and credit as- 
signment indicates where it failed. Heuristics will indicate 
which thematic properties are associated with each argu- 
ment, and stage (3) links the arguments with the proper 
roles, according to Canonical Mapping. This links Mary 
to Agent and the cat to Patient. 
One important point to make here is that any 
information from the perceptual expression that is not 
grammatically expressed will automatically be assumed 
to be part of the verb meaning itself. In this case, the 
instrument of the hitting (e.g. Mary's arm) is covered by 
the lexical semantics of hit. 
There are two forms of generalization performed 
by the system in step (5): constraint propagation and 
thematic decoupling. In a propagation procedure (Cf. 
\[Waltz, 1975\]), the computation is described as operat- 
ing locall!/, since the change has local consistency. To 
illustrate, consider the verb entry for have, as in (1), 
(I) John has a book. have(z =/;, y = Th) 
where the object carries the feature \[-abstract\]. Now, con- 
sider how the sense of the verb changes with a feature 
change to \[~abetract\], as in (2). 
(2) John has an idea. 
In other words, there is a propagation of this feature to 
the subject, where the sense of locative becomes more 
abstract, e.g. menta/. These types of extensions give rise 
to other verbs with the same thematic mapping, but with 
~relaxed" interpretations. * 
The other strategy employed here is that of the- 
matic decoupling, where thematic information becomes 
disassociated from the lexical semantics for a verb. ' 
The narrower interpretation of a verb's meaning will be 
arrived at after enough training instances are given; for 
example, from cut as meaning a particular action with a 
knife, to cut as an action that results in a certain state. 
It is interesting to speculate on how these strate- 
gies facilitate the development from perceptual relations 
to more abstract ones. The verb tell, for example, can be 
viewed as a transfer verb with a \[+abstract\] Theme, and the 
accompanying contraint propagation (Cf. \[Pinker, 1984\] 
and \[Jackendoff, 1983\]). Similarly, experiencer verbs such 
as please, upset, and anger can be seen as combining both 
strategies: they are similar to transfer verbs, but with lea- 
s For further discussion of constraint propagation as 
a learning strategy, cf. Pustejovsky (1987b). 
9 Results given in Nygren (1977) indicate that chil- 
dren have fully incorporated instruments for verbs such 
as hammer, cut, and saw, and only at a later.age do they 
abstract to a verb sense without a particular and constant 
instrument interpretation. 
ture relaxation on the Theme, together with propagated 
constraints to the Source and Goal (the subject and ob- 
ject, respectively); the difference is that the Theme is 
incorporated said is not grammatically expressed. 
John pleased his mother. 
please(z ~ ~, y ffi G, Th : incorporated) 
Conclusions 
In this paper we have outlined a theory of acquisi- 
tion for the semantic roles associated with verbs. Specifi- 
cally, we argue that perceptual predicates form the foun- 
dation for later conceptual development in language, and 
propose a specific algorithm for learning employing a the- 
ory of markedness for thematic types and the two strate- 
gies of thematic decoupling and constraint relazation and 
propagation. The approach sketched above will doubtless 
need revision and refinement on particular points, but is 
claimed to offer a new perspective which can contribute to 
the solution of some long-standing puzzles in acquisition. 
Acknowledgements 
I would like to thank Sabine Bergler who did the 
first implementation of the algorithm, as well as Anthony 
Maddox, John Brolio, Ken Wexler, Mellissa Bowermxn, 
and Edwin Williams for useful discussion. All faults and 
errors are of course my own. 
References 
\[I\] Angluin, D. "Inductive Inference of formal Lan- 
guages from positive data." In\[ormation and Con- 
trol 45:117-135. 
\[2\] Berwick, Robert C. The Acquisition of Syntactic 
Information, MIT Press, Cambridge, MA. 1985. 
\[3\] Berwick, Robert C., "Learning from Positive-Only 
Examples: The Subset Principle and Three Case 
Studies," in Michalski et al, 1986. 
\[4\] Bowerman, Mellissa "Learning the Structure of Cau 
satire Verbs," in Clark (ed) Papers and reports on 
child language development, No. 8, Stanford Uni- 
versity Committee on Linguistics. 1974 
\[5\] Chomsky, Noam Rules and Representation, Colum- 
bia University Press, 1980 
\[6\] Chomsky, Noam Lectures on Government and Bind- 
ing, Foris, Holland, 1981. 
\[7\] Dowry, David R., Word Meaning and Montague 
Grammar, D. Reidel, Dordrecht, Holland, 1979. 
\[8\] Jackendoff , Ray, Language and Cognition, MIT 
Press, Cambridge, MA. 1983. 
\[9\] Jackendoff, Ray, ~The Role of Thematic Relations 
in Linguistic Theory,", ms. Brandeis University, 
1985 
177 
\[I0\] Kodratoff, Yves, and J-G. Ganascia, "Improving 
the Generalization Step in Learning", in Michal- 
skiet el (eds.), Machine Learning II, Morgan Kauf- 
mann, 
\[11\] Marcus, Mltch, A Theory of Syntactic Recogni- 
tion for Natural Language, MIT Press, Cambridge, 
1980 
\[12\] Michalski, R.S., "A Theory and Methodology of 
Inductive Learning,", in Michalski et al (eds.), Ma- 
chins Learning L 
\[13\] Miller, George, "Dictionaries of the Mind" in Pro- 
ceedings of the 23rd Annual Meeting of the As- 
sociation for Computational Linguistics, Chicago, 
1985. 
\[14\] Miller, George and Philip Johnson-Laird, Language 
and Perception, Belknap, Harvard University Press, 
Cambridge, MA. 1976. 
\[15\] Mitchell, Tom, "Version Spaces: A Candidate Elim- 
ination Approach to Rule Learning," in IJCAI-77, 
1977 
\[16\] Mitchell, Tom, Version Spaces: An Approach to 
Concept Learning, Ph.D. thesis Stanford, 1978. 
\[17\] Nygren, Carolyn, "Results of Experiments with In- 
strumentals," ms. UMASS, Amherst, MA. 
\[18\] Pilato, Samuel F. and Robert C. Berwick, "Re- 
versible Automata and Induction of the English 
Auxiliary System", in Proceedings of the 23rd An- 
num Meeting of the Association for Computational 
Linguistics, Chicago, 1985. 
\[19\] Pinker, Steven, Lan#uage Learnability and Lan- 
guage D~velopmcnt, Harvard University Press, Cam 
bridge, 1984 
\[20\] Pustejovsky, James, "A Theory of Lexical Seman- 
tics for Concept Acqusition in Natural Language", 
to appear in /n~ernatioaa/Journal of Intelligent Systems 
\[21\] Pustejovsky, James and Sabine Bergler, "On the 
Acquisition of the Conceptual Lexicon", paper sub- 
mitted to AAAI-1987, Seattle, WA. 
\[22\] Slobin , D. "Universals and Particulars in Lan- 
guage Acqusition", in Gleitmann, Language Ac- 
quisition, Cambridge, 1982 
\[23\] Waltz, David "Understanding line drawings of sce- 
nces with shadows," in The Psychology of Com- 
puter Vision, P. Winston ed. New York, McGraw- 
Hill, pp. 19-92. 
\[24\] Waltz, David "Event Space Descriptions," Pro- 
ceedings of the AAAI-82, 1982 
\[25\] Williams, Edwin, "Predication", Linguistic Inquiry, 
1980 
\[26\] Winston, Patrick H., "Learning by Augmenting 
Rules and Accumulating Censors," in Michalski et 
al, 1986. 
\[27\] Winston, Patrick, Binford, Katz, and Lowry, "Learn 
ing Physical Descriptions from Functional Defini- 
tions, Examples, and Precedents, Proceedings of 
AAAI, Washington, 1983 
178 
