AUTOMATIC ACQUISITION OF THE LEXICAL SEMANTICS OF VERBS 
FROM SENTENCE FRAMES* 
Mort Webster and Mitch Marcus 
Department of Computer and Information Science 
University of Pennsylvania 
200 S. 33rd Street 
Philadelphia, PA 19104 
ABSTRACT 
This paper presents a computational model of verb 
acquisition which uses what we will call the princi- 
ple of structured overeommitment to eliminate the 
need for negative evidence. The learner escapes 
from the need to be told that certain possibili- 
ties cannot occur (i.e., are "ungrammatical") by 
one simple expedient: It assumes that all proper- 
ties it has observed are either obligatory or for- 
bidden until it sees otherwise, at which point it 
decides that what it thought was either obliga- 
tory or forbidden is merely optional. This model 
is built upon a classification of verbs based upon 
a simple three-valued set of features which repre- 
sents key aspects of a verb's syntactic structure, 
its predicate/argument structure, and the map- 
ping between them. 
1 INTRODUCTION 
The problem of how language is learned is per- 
haps the most difficult puzzle in language under- 
standing. It is necessary to understand learning in 
order to understand how people use and organize 
language. To build truly robust natural language 
systems, we must ultimately understand how to 
enable our systems to learn new forms themselves. 
Consider the problem of learning new lexical 
items in context. To take a specific example, how 
is it that a child can learn the difference between 
the verbs look and see (inspired by Landau and 
Gleitman(1985) )? They clearly have similar core 
meanings, namely ~perceive by sight". One ini- 
tially attractive and widely-held hypothesis is that 
*This work was partially supported by the DARPA 
grant N00014-85-K0018, and Alto grant DAA29-84-9- 
0027. The authors also wish to thank Beth Levin and the 
anonymotm reviewers of this paper for many helpful com- 
ments. We ~ b~efit~l greatly from disctumion of issues 
of verb acquisition in children with Lila Gleitman. 
word meaning is learned directly by observation 
of the surrounding non-linguistic context. While 
this hypothesis ultimately only begs the question, 
it also runs into immediate substantive difficulties 
here, since there is usually looking going on at the 
same time as seeing and vice versa. But how can 
one learn that these verbs differ in that look is 
an active verb and see is stative? This difference, 
although difficult to observe in the environment, 
is clearly marked in the different syntactic frames 
the two verbs are found in. For example, see, be- 
ing a stative perception verb, can take a sentence 
complement: 
(1) John saw that Mary was reading. 
while look cannot: 
(2) * John looked that Mary was reading. 
Also look can be used in an imperative, 
(3) Look at the ball! 
while it sounds a bit strange to command someone 
to see, 
(4) ? See the ball! 
(Examples like "look Jane, see Spot run!" 
notwithstanding.) This difference reflects the fact 
that one can command someone to direct their 
eyes (look) but not to mentally perceive what 
someone else perceives (see). As this example 
shows, there are clear semantic differences between 
verbs that are reflected in the syntax, but not ob- 
vious by observation alone. The fact that children 
are able to correctly learn the meanings of look and 
see, as well as hundreds of other verbs, with mini- 
mal exposure suggests that there is some correla- 
tion between syntax and semantics that facilitates 
the learning of word meaning. 
Still, this and similar arguments ignore the fact 
that children do not have access to the negative 
177 
evidence crucial to establishing the active/stative 
distinction of the look/see pair. Children cannot 
know that sentences like (2) and (4) do not oc- 
cur, and it is well established that children are 
not corrected for syntactic errors. Such evidence 
renders highly implausible models like that of 
Pinker(198?), which depend crucially on negative 
examples. How then can this semantic/syntactic 
correlation be exploited? 
STRUCTURED OVERCOM- 
MITMENT AND A LEARNING 
ALGORITHM 
In this paper, we will present a computational 
model of verb acquisition which uses what we will 
call the principle of structured o~ercomrnitment to 
eliminate the need for such negative evidence. In 
essence, our learner learns by initially jumping to 
the strongest conclusions it can, simply assum- 
ing that everything within its descriptive system 
that it hasn't seen will never occur, and then later 
weakening its hypotheses when faced with contra- 
dictory evidence. Thus, the learner escapes from 
the need to be told that certain possibilities can- 
not occur (i.e. are"ungrammatical') by the simple 
expedient of assuming that all properties it has ob- 
served are either always obligatory or always for- 
bidden. If and when the learner discovers that 
it was wrong about such a strong assumption, it 
reclassifies the property from either obligatory or 
forbidden to merely optional. 
Note that this learning principal requires that 
no intermediate analysis is ever abandoned; anal- 
yses are only further refined by the weakening of 
universals (X ALWAYS has property P) to existen- 
rials (X SOMETIMES has property P). It is in this 
sense that the overcommitment is"structured." 
For such a learning strategy to work, it must be 
the case that the set of features which underlies the 
learning process are surface observable; the learner 
must be able to determine of a particular instance 
of (in this case) a verb structure whether some 
property is true or false of it. This would seem to 
imply, as far as we can tell, a commitment to the 
notion of em learning as selection widely presup- 
posed in the linguistic study of generative gram- 
mar (as surveyed, for example, in Berwick(1985). 
Thus, we propose that the problem of learning the 
category of a verb does not require that a natu- 
ral language understanding system synthesize em 
de novo a new structure to represent its seman- 
tic class, but rather that it determine to which of 
a predefined, presumably innate set of verb cate- 
gories a given verb belongs. In what follows below, 
we argue that a relevant classification of verb cat- 
egories can be represented by simple conjunctions 
of a finite number of predefined quasi-independent 
features with no need for disjunction or complex 
boolean combinations of features. 
Given such a feature set, the Principal of Struc- 
tured Overcommitment defines a partial ordering 
(or, if one prefers, a tangled hierarchy) of verbs as 
follows: At the highest level of the hierarchy is a 
set of verb classes where all the primary four fea- 
tures, where defined, are either obligatory or for- 
bidden. Under each of these "primary" categories 
there are those categories which differ from it only 
in that some category which is obligatory or for- 
bidden in the higher class is optional in the lower 
class. Note that both obligatory and forbidden 
categories at one level lead to the same optional 
category at the next level down. 
The learning system, upon encountering a verb 
for the first time, will necessarily classify that verb 
into one of the ten top-level categories. This is be- 
cause the learner assumes, for example, that if a 
verb is used with an object upon first encounter, 
that it always has an object; if it has no object, 
that it never has an object, etc. The learner will 
leave each verb classification unchanged upon en- 
countering new verb instances until a usage occurs 
that falsifies at least one of the current feature val- 
ues. When encountering such a usage i.e. a verb 
frame in which a property that is marked obliga- 
tory is missing, or a property that is marked for- 
bidden is present (there are no other possibilities) 
- then the learner reclassifies the verb by mov- 
ing down the hierarchy at least one level replacing 
the OBLIGATORY or FORBIDDEN value of that 
feature with OPTIONAL. 
Note that, for each verb, the learner's classifica. 
tion moves monotonically lower on this hierarchy, 
until it eventually remains unchanged because the 
learner has arrived at the correct value. (Thus 
this learner embodies a kind of em learning in the 
limit. 
3 THE FEATURE SET AND THE 
VERB HIERARCHY 
As discussed above, our learner describes each 
verb by means of a vector of features. Some 
of these features describe syntactic properties 
of the verb (e.g."Takes an Object"), others de- 
scribe aspects of the theta-structure (the predi- 
cate/argument structure) of the verb (e.g."Takes 
178 
an Agent",~Ikkes a Theme"), while others de- 
scribe some key properties of the mapping be- 
tween theta-structure and syntactic structure 
(e.g."Theme Appears As Surface Object"). Most 
of these features are three-valued; they de- 
scribe properties that are either always true (e.g. 
that"devour" always Takes An Object), always 
false (e.g. that "fall" never Takes An Object) or 
properties that are optionally true (e.g. that"eat" 
optionally Takes An Object). Always true values 
will be indicated as"q-" below, always false values 
as"-" and optional values as~0 ". 
All verbs are specified for the first three 
features mentioned above: "Takes an Object" 
(OBJ),"Takes an Agent" (AGT), and"Takes a 
Theme" (THEME). All verbs that allow OBJ and 
THEME are specified for"Theme Appears As Ob- 
ject" (TAO), otherwise TAO is undefined. At the 
highest level of the hierarchy is a set of verb classes 
where all these primary features, where defined, 
are either obligatory or forbidden. Thus there are 
at most 10 primary verb types; of the eight for the 
first three features, only two (-I--q-, and -H-+) 
split for TAO. 
The full set of features we assume include the 
primary set of features (OBJ, AGT, THEME, and 
TAO), as described above,,and a secondary set of 
features which play a secondary role in the learn- 
ing algorithm, as will be discussed below. These 
secondary features are either thematic properties, 
or correlations between thematic and syntactic 
roles. The thematic properties are: LOC - takes a 
locative; INST - takes an instrument; and DAT - 
takes a dative. The first thematic-syntactic map- 
ping feature "Instrument as Subject" is fake if 
no instrument can. appear in subject position (or, 
true if the subject is always an instrument, al-" 
though this is never the case.) The second such 
feature "Theme as Chomeuf (TAC) is the only 
non-trinary-valued feature in our learner; it spec- 
ifies what preposition marks the theme when it is 
not realized as subject or object. This feature, if 
not -, either takes a lexical item (a preposition, 
actually, as its value, or else the null string. We 
treat verbs with double objects (e.g. "John gave 
Mary the ball.") as having a Dative as object, and 
the theme as either marked by a null preposition 
or, somewhat alternatively, as a bare NP chomeur. 
(The facts we deal with here don't decide between 
these two analyses.) 
Note that this analysis does not make explict 
what can appear as object; it is a claim of the 
analysis that if the verb is OBJ:÷ or OBJ:0 and is 
TAO:- or TAO:0, then whatever other thematic 
roles may occur can be realized as the object. This 
may well be too strong, but we are still seeking a 
counterexample. 
Figure 1 shows our classification of some verb 
classes of English, given this feature set. (This 
classification owes much to Levin(1985), as well as 
to Grimshaw(1983) and Jackendoff(1983).) This is 
only the beginning of such a classification, clearly; 
for example, we have concentrated our efforts 
solely on verbs that take simple NPs as comple- 
ments. Our intention is merely to provide a rich 
enough set of verb classes to show that our clas- 
sification scheme has merit, and that the learning 
algorithm works. We believe that this set of fea- 
tures is rich enough to describe not only the verb 
classes covered here but other similar classes. It is 
also our hope that an analysis of verbs with richer 
complement structures will extend the set of fea- 
tures without changing the analysis of the classes 
currently handled. 
It is interesting to note that although the partial 
ordering of verb classes is defined in terms of fea- 
tures defined over syntactic and theta structures, 
that there appears to be at least a very strong se- 
mantic reflex to the network. Due to lack of space, 
we label verb cla-~ses in Figure 1 only with exem- 
plars; here we give a list of either typical verbs in 
the class, and/or a brief description of the class, 
in semantic terms: 
• Spray, load, inscribe, sow: Verbs of physical 
contact that show the completive/noncomple- 
tire 1 alternation. If completive, like "fill". 
• Clear, empty: Similar to spray/load, but if 
completive, like "empty". 
• Wipe: Like clear, but no completive pattern. 
• Throw: The following four verb classes all in- 
volve an object and a trajectory. '~rhrow" 
verbs don't require a terminus of the trajec- 
tory. 
• Present: Like "throw", as far as we can tell. 
• Give: Requires a terminus. 
z This is the differ~ce between: 
I \]osded.the hay on the truck. 
sad 
I loaded the truck with hay. 
In the second case, but not the first, them is a implication that the truck is completely full. 
179 
SPRAY, 
LOAD 
EMPTY 
SEARCH 
BREAK, 
DESTROY 
TOUCH 
PUT 
DEVOUR 
FLY 
BREATHE 
FILL 
GIVE 
T 
FLOWER 
IO'+IA°TI+*'--+ IT+O II++01°'TID TI ,*+T, I s~ I 
I + I + n o i o ii o i o i - i ~th i o i 
Im~EilHili~iN/_ii 
JR iE~i,i i+ilU i'Pi B ~ ~ 
;'Hi -- ~,i~__ --i---'----+--~~ 
IIn -- -- --|illi -- --I~,J 
+i mmmi ~ imPili-im m,i il-i i-emiR 
~ ~ ~ i+i I Emili~m i-in ii.i im 
i ........ |/ i i I b 
-m i~ ~ irJil ~io iil-i i~ i,.il ii 
|D~NI~E 
÷ 
,|n-- iailio| .... 
t.m,__~__ 
:I + - i,l ..... I --\[+l~-- 
:'mi,i i~m,im .... ~~ 
~m ~ ~ m-roll mill mira mm mmlm i 
Figure 1: Some verb feature descriptions. 
( .... ) 
I~wAYs l 
1. 
(--0.) 
(-+--) 
IS~-IM.mm~ 
(+++o) 
IALWAYS +m~.~.l 
(*+.0.) 1+1 
(00+0) 
(..~ .) (..÷÷) (+-++) 
( +,,..*+ .,,. 0 ) ( .+. O.t. +. ) 
"Ik 
( 0 + .,,+ O) iqJSH (++00) 
F j=-++ 1 
10+001 
~t ~ qlmul 
I 
Figure 2: The verb hierarchy. 
180 
• Poke, jab, stick, touch: Some object follows a 
trajectory, resulting in surface contact. 
• Hug: Surface contact, no trajectory. 
• Fill: Inherently ¢ompletive verbs. 
• Search: Verbs that show a completive/non- 
completive alternation that doesn't involve 
physical contact. 
• Die, flower: Change of state. Inherently non- 
agentive. 
• Break: Change of state, undergoing causitive 
alternation. 
• Destroy: Verbs of destruction. 
• Pierce: Verbs of destruction involving a tra- 
jectory. 
* Devour, dynamite: Verbs of destruction with 
incorporated instruments 
• Put: Simple change of location. 
• Eat: Verbs of ingesting allowing instruments 
• Breathe: Verbs of ingesting that incorporate 
instrument 
• Fall, swim: Verbs of movement with incorpo- 
rated theme and incorporated manner. 
• Push: Exerting force; maybe something 
moves, maybe not. 
• Stand: Like "break s, but at a location. 
• Rain: Verbs which have no agent, and incor- 
porate their patient. 
The set of verb classes that we have investigated 
interacts with our learning algorithm to define the 
partial order of verb classes illustrated schemati- 
cally in Figure 2. 
For simplicity, this diagram is organized by the 
values of the four principle features of our system. 
Each subsystem shown in brackets shares the same 
principle features; the individual verbs within each 
subsystem differ in secondary features as shown. 
If one of the primary features is made optional, 
the learning algorithm will map all verbs in each 
subsystem into the same subordinate subsystem 
as shown; of course, secondary feature values are 
maintained as well. In some cases, a sub-hierarchy 
within a subsystem shows the learning of a sec- 
ondary feature. 
We should note that several of the primary verb 
classes in Figure 2 are unlabelled because they cor- 
respond to no English verbs: The class "----" 
would be the class of rain if it didn't allow forms 
like ~hail stones rained from the sky", while the 
class '~+--I--t-" would be the class of verbs like "de- 
strof' if they only took instruments as subjects. 
Such classes may be artifacts of our analysis, or 
they may be somewhat unlikely classes that are 
filled in languages other than English. 
Note that sub-patterns in the primary feature 
subvector seem to signal semantic properties in a 
straightforward way. So, for example, it appears 
that verbs have the pattern {OBJ:+, THEME:+, 
TAO:-} only if they are inherently completive; 
consider "search" and "fill". Similarly, the rare 
verbs that have the pattern {OBJ:-, THEME:-}, 
i.e those that are truly intransitive, appear to in- 
corporate their theme into their meaning; a typi- 
cal case here is =swim". Verbs that are {OBJ:-, 
AGT:-} (e.g. =die") are inherently stative; they 
allow no agency. Those verbs that are {AGT:+} 
incorporate the instrument of the operation into 
their meaning. We will have to say about this be- 
low. 
4 THE LEARNING ALGORITHM 
AT WORK 
Let us now see how the learning algorithm works 
for a few verbs. 
Our model presupposes that the learner receives 
as input a parse of the sentence from which to de- 
rive the subject and object grammatical relations, 
and a representation of what NPs serve as agent, 
patient, instrument and location. This may be 
seen as begging the question of verb acquisition, 
because, it may be asked, how could an intelligent 
learner know what entities function as agent, pa- 
tient, etc. without understanding the meaning of 
the verb? Our model in fact presupposes that a 
learner can distinguish between such general cat- 
egories as animate, inanimate, instrument, and 
locative from direct observation of the environ- 
ment, without explicit support from verb meaning; 
i.e. that it will be clear from observation em who is 
acting on em what em where. This assumption is 
not unreasonable; there is strong experimental ev- 
idence that children do in fact perceive even some- 
thing as subtle as the difference between animate 
and inanimate motion well before the two word 
stage (see Golinkoff et al, 1984). Thisnotion that 
agent, patient and the like can be derived from 
direct observation (perhaps focussed by what NPs 
181 
appear in the sentence) is a weak form of what 
is sometimes called the em semantic bootstrap- 
ping hypothesis (Pinker(1984)). The theory that 
we present here is actually a combination of this 
weak form of semantic bootstrapping with what is 
called em syntactic bootstrapping, the notion that 
syntactic frames alone offer enough information to 
classify verbs (see Naigles, Gleitman, and Gleit- 
man (in press) and Fisher, Gleitman and Gleit- 
man(1988).) 
With this preliminary out of the way, let's turn 
to a simple example. Suppose the learner encoun- 
ters the verb "break", never seen before, in the 
context 
(6) The window broke. 
The learner sees that the referent of "the window" 
is inanimate, and thus is the theme. Given this 
and the syntactic fzarne of (6), the learner can see 
that em break (a) does not take an object, in this 
case, (b) does not take an agent, and (c) takes 
a patient. By Structured Overcommitment, the 
learner therefore assumes that em break em never 
takes an object, em never takes a subject, and em 
always takes a patient. Thus, it classifies em break 
as {OBJ:-, AGT:-, THEME:+, TAO:-} (ifTAO 
is undefined, it is assigned "-'). It also assumes 
that em break is {DAT:-, LOC:-, INST:-, ... } 
for similar reasons. This is the class of DIE, one 
of the toplevel verb classes. 
Next, suppose it sees 
(7) John broke the window. 
and sees from observation that the referent of 
"John" is an agent, the referent of "the window" 
a patient, and from syntax that "John" is sub- 
ject, and "the window" object. That em break 
takes an object conflicts with the current view that 
em break NEVER takes an object, and therefore 
this strong assumption isweakened to say that 
em break SOMETIMES takes an object. Simi- 
larly, the learner must fall back to the position 
that em break SOMETIMES can have the theme 
serve as object, and can SOMETIMES have an 
agent. This takes {OBJ:-, AGT:-, THEME:+, 
TAO:-} to {OBJ:0, AGT:0, THEME:+, TAO:0}, 
which is the class of both em break and em stand. 
However, since it has never seen a locative for ern 
break, it assumes that em break falls into exactly 
the category we have labelled as "break".2 
2And how would it distinguish between 
The vase stood on the table. 
mad 
There are, of course, many other possible orders 
in which the learner might encounter the verb em 
break. Suppose the learner first encounters the 
pattern 
(8) John broke the window. 
beR)re any other occurrences of this verb. Given 
only (8), it will assume that em break always takes 
an object, always takes an agent, always has a pa- 
tient, and always has the patient serving as ob- 
ject. The learner will also assume that em break 
never takes a location, a dative, etc. This will 
give it the initial description of {OBJ:+, AGT:+, 
THEME:+, TAO:+, ..., LOC:-), which causes 
the learner to classify em break as falling into 
the toplevel verb class of DEVOUR, verbs of de- 
struction with the instrument incorporated into 
the verb meaning. 
Next, suppose the learner sees 
(9) The hammer broke the window. 
where the learner observes that '~hammer" is an 
inanimate object, and therefore must serve as in- 
strument, not agent. This means that the earlier 
assumption that agent is necessary was an over- 
commitment (as was the unmentioned assump- 
tion that an instrument was forbidden). The 
learner therefore weakens the description of em 
break to {OBJ:+, AGT:0, THEME:-{-, TAO:+, 
..., LOC:-, INST:0}, which moves em break into 
the verb class of DESTROY, destruction without 
incorporated instrument. 
Finally (as it turns out), suppose the learner 
sees 
(10) The window broke. 
Now it discovers that the object is not obliga- 
tory, and also that the theme can appear as sub- 
ject, not object, which means that TAO is op- 
tional, not obligatory. This now takes em break to 
{OBJ:0, AGT:0, THEME:+, TAO:0, ... }, which 
is the verb class of break. 
We interposed (9) between (8) and (10) in this 
sequence just to exercise the learner. If (10) fol- 
lowed (8) directly, the learner would have taken em 
break to verb class BREAK all the more quickly. 
Although we will not explicitly go through the ex- 
ercise here, it is important to our claims that any 
permutation of the potential sentence frames of em 
break will take the learner to BREAK, although 
some combinations require verb classes not shown 
The base broke on the table? 
This is a probl~n we discuss at the end of this paper. 
182 
on our chart for the sake of simplicity (e.g. the 
class {OBJ:0, AGT:-, THEME:+, TAO:0} if it 
hasn't yet seen an agent as subject.). 
We were somewhat surprised to note that the 
trajectory of em break takes the learner through a 
sequence of states whose semantics are useful ap- 
proximations of the meaning of this verb. In the 
first case above, the learner goes through the class 
of "change of state without agency", into the class 
of BREAK, i.e. "change of state involving no lo- 
cation". In the second case, the trajectory takes 
the learner through "destroy with an incorporated 
instrument", and then DESTROY into BREAK. 
In both of these cases, it happens that the trajec- 
tory of em break through our hierarchy causes it 
to have a meaning consistent with its final mean- 
ing at each point of the way. While this will not 
always be true, it seems that it is quite often the 
case. We find this property of our verb classifica- 
tion very encouraging, particularly given its gene- 
sis in our simple learning principle. 
We now consider a similar example for a dif- 
ferent verb, the verb em load, in somewhat terser 
form. And again, we have chosen a somewhat indi- 
rect route to the final derived verb class to demon- 
strate complex trajectories through the space of 
verb classes. Assume the learner first encounters 
(II) John loads the hay onto the truck. 
From (11), the learner builds the representa- 
tion {OBJ:+, AGT:+, THEME:+, TAO:+, ..., 
LOC:+, ..., DAT:-}, which lands the learner into 
the class of PUT, i.e. "simple change of location". 
We aasume that the learner can derive that "the 
truck" is a locative both from the prepositional 
marking, and from direct observation. 
Next the learner encounters 
(12) John loads the hay. 
From this, the learner discovers that the location 
is not obligatory, but merely optional, shifting 
it to {OBJ:+, AGT:+, THEME:+, TAO:+, ..., 
LOC:O ..., DAT:-}, the verb class of HUG, with 
the general mean/ng of "surface contact with no 
trajectory." 
The next sentence encountered is 
(13) John loads the truck with hay. 
This sentence tells the learner that the theme need 
only optionally serve as object, that it can be • 
shifted to a non-argument position marked with 
the preposition em with. This gives em load 
the description of {OBJ:+, AGT:+, THEME:+, 
TAO:0, TAC:with, ..., LOC:0 .... DAT:-}. This 
new description takes em load now into the verb 
class of POKE/TOUCH, surface contact by an 
object that has followed some trajectory. (We 
have explicitly indicated in our description here 
that {DAT:-} was part of the verb description, 
rather than leaving this fact implicit, because we 
knew, of course, that this feature would be needed 
to distinguish between the verb classes of GIVE 
and POKE/TOUCH. We should stress that this 
and many other features are encoded as "-" until 
encountered by the learner; we have simply sup- 
pressed explicitly representing such features in our 
account here unless needed.) 
Finally, the learner encounters the sentence 
(14) John loads the truck. 
which makes it only optional that the theme 
must occur, shifting the verb representation to 
{OBJ:+, AGT:+, THEME:0, TAO:0, TAC:with, 
..., LOC:0 ..., DAT:-}. The principle four fea- 
tures of this description put the verb into the gen- 
eral area of WIPE, CLEAR and SPRAY/LOAD, 
but the optional locative, and the fact that the 
theme can be marked with em with select for the 
class of SPRAY/LOAD, verbs of physical contact 
that show the completive/noncompletive alterna- 
tion: 
Note that in this case again, the semantics of the 
verb classes along the learning trajectory are rea- 
sonable successive approximations to the meaning 
of the verb. 
5 FURTHER RESEARCH AND 
SOME PROBLEMS 
One difficulty with this approach which we have 
not yet confronted is that real data is somewhat 
noisy. For example, although it is often claimed 
that Motherese is extremely clean, one researcher 
has observed that the verb "put", which requires 
both a location and an object to be fully grammat- 
ical, has been observed in Motherese (although 
extremely infrequently) without a location. We 
strongly suspect, of course, that the assumption 
that one instance suffices to change the learner's 
model is too strong. It would be relatively easy 
to extend the model we give here with a couple 
of bits to count the number of counterexamples 
seen for each obligatory or forbidden feature, with 
two or three examples needed within some limited 
time period to shift the feature to optional. 
Can the model we describe here be taken as a 
psychological model? At first glance, clearly not, 
183 
because this model appears to be deeply conser- 
vative, and as Pinker(1987) demonstrates, chil- 
dren freely use verbs in patterns that they have 
not seen. In our terms, they use verbs as if they 
had moved them down the hierarchy without ev- 
idence. The facts as currently understood can be 
accounted for by our model given one simple as- 
sumption: While children summarize their expo- 
sure to verb usages as discussed above, they will 
use those verbs in highly productive alternations 
(as if they were in lower categories) for some pe- 
riod after exposure to the verb. The claim is that 
their em usage might be non-conservative, even 
if their representations of verb class are. By this 
model, the child would restrict the usage of a given 
verb to the represented usages only after some pe- 
riod of time. The mechanisms for deriving criteria 
for productive usage of verb patterns described by 
Pinker(1987) could also be added to our model 
without difficulty. In essence, one would then 
have a non-conservative learner with a conserva- 
tive core. 
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Fisher, C.; Gleitman, H.; and Gleitman, L. 
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