Figure 15

Some possible network grammars

 

m7 , oN
THE = NOUN > STOP

oO EEO __ Se STOP
~~ STOP

51
Every bit as much as LAS, 4 enild logically needs negative information ta
reeover Trom overgeneralizations. The interesting quest2on igs where the negative
information comes from in the case of the child. Parents ao correct the cnild

in such cov ious morphemic overgeneralizations (Brown, 1973). Even today x

find myself corrected (not by my parents ) for my failures to properly pluralize
esoteric words. ‘The child may also Use statistical evidence for a negative con
elusion. In some manner ne may. notice shat the morphesic form foots is never
used by the aduls and so conclude that 1% is wrong. Horning (1969) has formalized
an algorithm for detecting such overgeneralizations py assigning probabilities

to rules.

vw

Figure 16 illustrates LAS's treatment o
training sequences. These involve some thre
sion of the noun phrases on the brancn of the start network for RB re
As can be seen from Figure 13, at the point of the hth sentence LAS has
its grammar to the point where it will nandle 616 sentences of the target lan-
ZuaZe. Actually the grammer has produced some overgeneralizations—-i

ept a total of 750 sentences. LAS has encountered phrases like square,

f the last four sentences
e word noun phrases and also expan-
1

 

 

ace

square small, square red, and square red small. From this experience, LAS
has generalized to the conclusion that the sentences of the language consist
of a shape, followed optionally by either @ size or color, followed optionally
py @ size Thus the induced grammar includes phrases Like squares small small
because ptable in poth second and third posi-

size words were found to be acce

erestingly, bhis mnisbake will nov cause LAS any problems. It will

phrase like square small small beceuse it will never have a to-

structure with.two smalls modifying an object. it will never
so and thus UNDERSTAND can nov moxe any mistakes. This is

how an over-general grammar can be successfully constrained

of semantic acceptadility-

hee QO

a
fo

never spea

hasspoken HAM

hear such @ pArese
mo}

@ nice exaipic

fhe problen of learning to sequence roun modifiers has turned out to be
a source of unexpected difficulty. in part, the oréering of modifiers is
governed by pragmatic factors, For instance one is likely to say small red

square when yeferring to one of many red squares, but red small squer when
referring to one of many small squares.. Differences like tnese could be
ef om

controlled by ordering of Links in the HAY memory structure.
G2NERALIZE .

After teking in 14 sentences LAS has built up 2 partial network grammar
shat serves to generate many more sentences than those it originally encountered.
However, note that LAS has constructed four copies of a noun phrase grazer.

One would Like it to recognize that those grammars ere the same. The failure

to do so with respect to this simple artificial language only amounts to an
inelegance. However, the identification of identical networks is eritical to
inducing languages with recursive rules.

52
A AAT+SI wr ook PAS sr y ad
Additions to LAS’s grammar atu:

  
   

      

 

 

 

 

 

 

 

 

 

16. SQUARE BLUE SMALL ERIANGLE
Tle PRIANG ® RED SQUARE BLUE LEP P-OF
12. TRIANGLE S SMALG SQUARE REO
13° SQUARE BLUS PRira NGLE BLUE e
Lhe SQU ARS RED LARGE TRIANGLE RED LARGE SELOW
oY 4 i)
NIL ;
STOP
E— 8593 D1095
B56 6-6 SOP
KS NIL
S>STOP
£5580 DLO23
B564 Se D1 OL ee STOP
NIL
peer STOP
conn he
p10232 2S El 94 —FL208__s =>STOP
ON Nib
SeSTOP
€D1117 E884
D1LO95 > E90L- SST OP
NIL
TOP
Sp71l4
D692 ———-————p STOP
EDLOLS
D1 095—£ PLY _s»st0P D1023 DLONS ss POP
E
298h —© E205 s~ STOP £1368 E22 see stor
D714 = small
D1O45 = red, biue,small
DLiL? = plue,red
£905 = small,large
E1395 = large

23
Anderacoa -

 

ye, > NOUN

3 k

 

That is, there are four networks, NP, HPL, NP no and uP. whose structuce is in-
dicated by the eoove rewite rules. It Ts assumed that LAS has only experienced
three consecutive adjecvives and therefore SPE KTEST has only created three
embeddings. se critical inductive steo for LAS is to recognize iP, = iP...
This requires recognizing the jdentity of the word classes NOUN, and HOU. and
the word classes Add, a ADJ This will be done on the criterion of the
emaunt of overtan af” 7 classes. it also reauires recognition
that network 2Pp = Neg. Thus, to identify two networks méey require that tvo
other networks ce identified. The network HP 3 is only 2 subnetwork of HP.

So in the recursive jaensification of networks, GHVERALIZs will have to accept
a subnetwork relation pesween one network Like NP, whieh contains another Like
NP... The assumption is thet with sufficient experience the emoedded network
would become filled out to be the same 45 the embedding network. After NPL
hes been identified with WP2 HAM will have a new network structure where NP*

represents the amalgamation of NP1, NP2, and NP3.

NP > the NOUN
the ADJ NP*
P* + NOUN*
ADJ* NP*
Hote that new word classes NOUN* and ADS* have been created es the union of
the word classes NOUN2, NOUN3, NOUNL and of the classes ADJ2, ADJ3, respectively.
ENERALIZE was called to ruminate over the networks generated after the

first fourteen senvences. GENERALIZE succeeded in identifying AlL9> with ALOT.
As a consequence, network A195 replaced network Al9T at the position where 1t

 

 

ceurred in the START network (see Figure 312). Similarly, B566 was identified
with and replaced network pS564. Finally, B566 was identified wita and replaced

4

A195 througnout the START network. Te final effective grammar is illustrated
in Figure 17. Iv now handles all tne sentences of the grammer. it hendles
more sentences then the granmar that Was constructed after the fourteenth.

54
Ficure 1?

Phe final crass

 

 

B Sach
SPARE | TOO
S305 ee
B508 PeAL96—S ae peA198-~ EZ A199
B593
3366 DL 6 TP
NIL
ne STOP
€D1117
B10 ee 9g SP
NIL

STOP

— E905
E88 —————— ee STOP

B568 = below, Left-of
AL99 = above, rignt- ~or
B593 = square , triangle

D1117 = plue, read large, small
F905 = large, small

29
sentence. Tis is because the noun-parase network E556 has been expanded to
jncorporate all possible noun purases. sefore the generalizations, none or
~f} 4¢. c ~ , ot
e 4564, BOOS, ALQD, oF ALOT were complete. ‘The network B965 be-

 

a. e L c
types of languages. The first is the assumption of the correspo c at
the surface structure of the language and the semantic structure. This is
critical to BRACKET's identification of the surface structure of the sentence
woich is, in turn, critical to the proper embedding of parsing networks.
Second, there is the assumption of a semantics-induced equivalence of syntax.
This played a eritical role poth in the generalization of SPEAXTEST and of

a

GENERALIZE. It was noted with respect to pluralization that such seneralize-
tions can be in error and that children also tend to make such errors. However,
I would want to argue that, on. the whole, natural language is not perverse.
Therefore, most of tnose generalizations will turn out to be good decisions.
Cleariy, for languages to be learnable there must be some set f generaliza~
tions which are usually safe. The only question is whether LAS hes captured
the safe generalizations. .

Tne importance of semantics to child lenguege learning has been suggested
in various ways recently by many theoreticians (e.g., Bloom, 1970; Bowerman, .
1973; Brown, 1973; Schlesinger, 1971; and Sinclair-de Zwart, 1973), but there
has been littie offered in the way of concrete elgorithms to make explicit
tne contriputicn oF semantics. LAS. L is a Tirst small step to making thi s

contribution explicit.
Conclusion

This concludes the explanation of the algorithms to be used by LAS.1 for
language induction. In many ways the task faced by LAS. 1 is overly simplistic
and its algorithms are probably too efficient and free from information-pro-
cessing limitations. Therefore, the acquisition penavior of LAS. 1 does not
nirror in most respects that ofthe child. Later versions of this program will
attenst a more realistic simulation. Nonetheless, f think LAS.1 is a signifi-
cent step forward. The following are the significant contributions embodied
so far in LAS. l.

1. The transition network formalism has been interfaced with a set of
simple and psychologically realistic long term memory operations.
In this way we have bridled the unlimited Turing-computable power of
the augmented transition network.

2. A single grammaticel formalism has been created for generation and
- understanding. Thus, LAS only needs to induce one set of grammatical
rules.

3, Two important ways were jdentified in which a semantic referent helps

grammar induction. These were stated as the grapn deformation condi-
tion and the semantics~induced equivalence of syntax conditions.

56
L, Algovrithzs have deen developed adequate to learn natural

The general mode of developing the program LAS is as follows: A lanyvusge
learning situation is specified py a set of conditions. tn LAS, 2 it was
specified that LAS already know the meaning of the words and that it be given,
as input, sentences with HAM representations of their meaning. The semantic
domain was specified to be that constituted by geometric shapes. Cnee @ set
of conditions is svecified, 2a set of goals is specified. In LAS. 1 there was
only one real goal: to learn any natural-like language taat deserived the
domain. Once a set of goals 15 specified a plan of attack is sketched ont.
However, the problem is such that the details of that plea only evolve as we
attempt to imolement th i Inde2d many interesting
problems and ideas that in LAS. 1 were discovered
in attempting ea impl ity of computer simulation
in theoretical develo

 

The LAS. 1 progr verated in a task domain which 2s
means identical, to that of atural language learning situation. Its benavior
was similar to © o earning a lenguage, but ezain by no means iden-
tical. In sre xn two yeers i propose to create a program LAS
considerably closer to sinuleting naturel language learning. I
elaborate set of goals than did LAS. 1:

   

ss __.
2whien comes
h

«

1. The program will incorporate realistic assumptions about short-term
menory limitations and left-to-right sentence processing.

2, The program will learn the meanings of words.

3. The program should use semantic and contextual redundancy to partially
replace exnlicitly provided HAM-encoding of pictures.

h, The program should handle sentences in a more complex semantic domain.

5. The progran should be elaborated to handle such things as questions
and commands as well as declarative sentences.

The general methods for achieving these goals in the LAS. 2 program will
be sketched out in the proposal section. Also in that section I wilt propose
some experiments to evaluate the LAS program. While it is true that the task
faced by LAS. 1 is not really natural language learning, it still is a learning
task at which hucan subjects apparently can succeed, The experiments will de-

termine whether humans have the same difficulties in such tasks as does LAS
and whether they make the same generalizations. However, I regard these exper-
inents as of secondary importance relative to program development. It is more
important to further articulate our understanding of what algorithms are ade-

quate for natural lenguage learning.

oT
 

 

It is probably inevitable tha
is really necessary to expend the c ry
program, Could not the model just be sp ecified nnn Tne reason 1 way
this is not possible has to do with the com roLexity of any theory that addresses
the details of natural language. There is no other way to test the predictions
of the theory or to assure tnat ly c¢ isten The experience
with large transformational gramin language is that
they have hidden inconsistencies. these ere only exposed by trying to simu-
late tne Eranmers on a computer (e.g., Friedman, 1971). Consider the deserip-
tion given of LAS. 1 in the pr eceding section? Although lacking in many details,
it was complex and lengthy. Could the reader esteblish for himself from tals
deseri ption whether the model is really internally consis stent? A computer

iw

c
program provides a proof of the consistency end @ means o2 determining &
model's behavior. The stated goals of this project are to develcp explicit
algorithms for natural languege learning veecify the relevant details of
these algorithms, and evaluate empirical ty tne & psye hological viability or

these algorithms. Without the use of computer simulation none of these goals

ane

could be achieved.
C, Methods of Procedure

First I will describe the proposed extension of the LAS program. Then I
cribe some experimental tests. In reading the specific extensions pro-

posed for LAS, the reader should keep in mind that they have ‘as their intent

e goals set forth in the preceding section.

achieving th

The Semantic Domain

 

The first matter to settle upon in the new progren is some semantic dem.in,
. ie relations 2 2

the LAL, tp wertd of ataves, prorperties, na s2cnstric Te-2Ulcns +2 20 surat
ished ror further work. Tne following is oroeposed as a suggestion altacugh
there is nothing critical ebout its exact form. It is critical, however, that
some semantic domain be chosen. It is only when there is a specified domein
that an explicit goal for success in the program can pe specified. The progran
will be regarded as successful if it can learn eny natural language describing

this domain.

I have chosen to look at a world close to that of a young child although
there is perhaps nothing sacred about this domain. This world is set forth in
Teble 5. There are three people in this world. In addition to these there are
four categories of objects--locations, containers, supporters, and toys.

These objects can have four types of properties--number, color, size, and quaii-
ty. Thus, LAS will have to deal seriously wita problemas of sequencing adjec-
tives. It will also have to deal with number es a property of objects. The
objects permit a much richer variety of reletions than in the vorid of LAS. 1.
This will provide a demanding test for the learning of complex multi-argumeat
relations. There can be sentences like Mommy traded Daddy the car for a ball.
In this world, people, containers, supporters, and toys can be in locations.
People can change their location and that of toys. People and toys can be on
supporters, toys can be in containers. People can possess toys, containers,

and supporters.

 

58
Anderson

TABLE 5

Categories in the World of LAS. 2

 

 

 

PEOPLE | LOCATIONS. COMPATUERS SUPPORTERS

Mommy bedroom box table

Daddy’ kitchen closet chair

LAS den dresser bed

TOYS NUMBERS COLORS. - SIZES GUALISIES
dolly one red big dirty

ear two blue media pretty
pall three green small shiny

Thus the differen: catexzories of objects enter differently into different types
of relations. This Te + will prove jmoortant to the predictive parsing facili-

ties that ZT will want to introduce into LAS. 2.

Left-te-Zisht Processing

Cnildran learn language auditorily. Thus, their induction algoritans must
process incoming material in a left-to-right manner. The current LEARVMORE
program does not go this. BRACKET completely processes the senten

SPEAKTEST even begins to work on it. Clearly, PRACKET an
integrated so that the beginning of the sentence is pracket

py SPEARTSST before the end of the sentence ts considered by eithe

ducing this left-to-right processing is a preliminary to introducing short-
term menory limitations into the induction situation.

Figure 18 illustrates in highly schematic form the left-to-right algorithn
proposed for LEARNMORE. Words are considered as they cone in from the sentence
LEARVMORE, as in UNDERSTAND, tries to find a path through its netvork grammar
to parse the sentence. The difference petween LEARNMORS enc UNDERSTAID is
that LEARNMORE hes available to it a HAM conceptual structure to enable it to
better evaluate various parsing options. Suppose LEARNMORE is at some point in
processing the sentence. It will also be at some point in 4 parsing n
Let us consider how it would process the next word. At box 2 it
in the word. At pox 3 it would set 1 to the various grameatical options (ares)
at that node in the network. Boxes Ty through 7 ere concerned witn evalua.
waether any of these options can handle the current word. Box 4 che
there are any options left. Box 5 sets a to the first option and re
the remaining options. Box 6 checks whether the word would be parse
and box 7 considers whether the action associated with that ere corr
a HAM structure. “If a passes the tests in 6 and 7, FARIMORES advances to con~
sidering the next word. Otherwise it tries another arc. Tf it exheus
arcs, it will call FIITDPATH (box 8) to build e new are fron the curreat node.

29
Flowen

wh
Me

rr
og

a

program

 

 

 

 

 

 

 

 

 

 

 

60

 
The work currently assigned to BRACKE? will have to be assigned to 9x [-
That is, box 7 will have to determine when an are snould involve @ push to en
embedded network and when it snould pop back up to an anbeddirg network. This
will be done by consulting tne information in the semantic struc vure. Tt would
also be possible to consult the pause structure of the sentence tor information
about phrase structure poundaries,

Note that certain sentences which the old LEARNMORE system could handle
will not be handled by this system. For instance, consider the sentence The
Square that is above the triangle is rignt-of the square. After the first two

 

 

 

 

 

3
words it would not be clea: which squer a
object or the subject of ; 3 a.
an appropriate action to the path. In tne old LEARNMORS thi
the referent of square Was resolved by let 2 n
* - « —_—__ .

dealing with it. Presumably, however, cal a
from such sentences.

 

bry
{p
[p+
3Q
fa
ct
t
Qo
rh £
H

t+ Om ©
}
ian .
ce
0
3
he
aes
ra
wy
o
Cc
h
joy

 

 

In this system it will not be assume that LAS knows the meaning of the
words, Rather this will be something that LAS will have to learn from the
pairing of sentences with conceptions. First let's discuss the learning of
words whose reference is a simpie concept or object, ©-&-> box or mommy, and
postpone discussion of c mpolex relational terms like trade. Logically, the
task of lexicalization is quite simple and it would not require complex algo-
rithms to succeed. For instance, consider this elgorithm: LAS is given a2
sentence with n, words and a conceptualization it Geseribes with ny concepts.

tore with each word the my, concepts. The next sentence that comes has Ro
words and its conceptualization consists of zp concepts. If a word in this sen-
tence is new, store with it the mp concepts. if the word is old, store with
it the intersection of the concepts previously stored with it and the new mo
concepts. Eventually, ignoring problems of polysemy, & word will become pared
down to zero or one concepts. Those with zero concepts are function words
end those with one concept have that concept as their meaning.

 

 

a
=
+
w

Of course, this elgorithm will ma into trouble if LAS does not always
eptualize all the concepts referred to by the sentenee, This can bea

died by having the algorithm wait for a sequence of disconrirming pieces
r idence before rejecting 2 hypothesized meaning. Incidentally, subjects
ehave just this way in concept attainment situations (see Bruner, Goodnow &
Austin, 1965), not teking negative evidence @&s having its full logical force
about the meaning of the word.

The basic problem with this algorithm is thet it makes unreasonable assunp—
ions about the information processing capacities of humans. In pilot researecn
£ my own, I have found that adult subjects can learn the meanings simultane-
ously of a number of words in a sentence. However, they do suffer difficulties
when there is high ambiguity about what a word means. Presumebly, children
would have even greater difficulties extracting word meanings from complex sen-
tences. Broen (1972) and Ferguson, Peizer, & Weeks (1973) report that new items
of vocabulary seemed to be introduced through use in set sentence frames such
as Where's ..., Here comes ...-, There's ... known as deitic phrases. The noun
tends to be heavily stressed and repeated. ‘The parent frequently points to help

 

61
ah env t
3, provided the child Knows

 
  
 
 

more complex Se nes g most of the ne
satical of the sentence fT. compine these yarious considera-
p L t eure 18 to deal

word is reac if Li ag %

about context and about the word's position in tae grammar, it {Ll co

‘this guess to menory and stick with the guess i ess later disconfirmed. Tne

program wilt only hazard a guess in circumstances of low uncertainty. Thus,

“4b will only guess if it can otherwise parse tne grammatical structure in which

the word appears. It will not guess if the word is receded or followed bY

know. Thus, the progres, much a8 adults appear to, will
contrasts between grammatical pattern and a
e program knows the grammatical rule NP - determiner

p>
a
Oo
rh
oct
Le
rt
Pe
[3

adjecti . the phrase the ¢lick box it will suppose thet
glick rerers to some property of the box.

Thus, the progren will have to acquire its initial vocabulary by means of
simple frames, 85 do young chilcren. With this initial vyocebulary information,
it can begin to learn grammatical rules. Once in possession of grammatical
rules, it will no longer nesd simple frames +o learn new lexical items.

One interesting question is how function words are ever identified as non-
meaning-pearing in this scheme, Presumably, 31 is done on the vasis of failing

a
d and any semantic yeature. FHLS
esses had been associated with a word,

 

So far I have assumed that all concepts are constructed before language

acquisition takes place and that the only problem is to link up these concepts

with words. But this is very unrealistic. Consider the verd give in the sen-
ives the dolly to Daddy. The meaning of give is something 1ike

 
   
    

ng
one to cease to vossess 3h object end someon’ elise
as ooject. Tt seens very implausible that a child comes
learning situation wits sucn a concept ready made. What probably —
he sees Mommy pushing the Goll to Daddy or Momny handing the

ball to vany. With these experiences he hears sentences like Mommy gives the

golly to Daddy or Mommy gives the pall to baby. From these examples he induces

the appropriate meaning of give. Cancept attainment in these situations can be
achieved by using the sort of concept jaentification used py Winston (1970) for
inducing geometric concepts. That is, each use of the word give is paired with

e EAM network structure given the meaning oF the sentence. Winston's heuristics
allow. us to extract what these network structures Pe
mon. ‘Tne concept give, es verb, is then attached to
For this sort of algorithm to succeed, LAS must be set to regerd certain con~
figurations of propositions, interlinked by causal terms, 4&5 being associated -
with a single relational term in the langu2ege.

 

 

62
   
    
  
  
 
   
  

Note also that the

meaning of complex re

  
     

~ % ~ abe as
e bae sentence moOmm
yA TT co
ERSTAND set upd,

 

 

 

 

 

 

 

      

 

 

na 2 caild is that
first the child
eh has been
in two and three
word ueterances. es ib appears
that children have omitt ad mo function + eonstructions.
One explanation of the origin of telegraph e pealing from the
point of view of LAS is the following: Suppose that LAS did not receive as input
to its Teena routine complete sentences lesrapnic sentences.
liy induce a te [t seexs reasoneole
otel sentence he
. If so, then his
be receiv as their basic
celesraghic
Shis necothesis com fron studies of chiid imitation of adult
mid bhay these tmdtati ons. amnile tanger than tha chiid's awm
iso telesraphic in nature (e.g., Srown 2 Fraser, 1964). Blas-
1970) found that childre tend to repezt those words which are
words which occur in terminal positions. The seme annem
eng to be stressed in adult speech. Scholes (1969, 970)
en tended to omit words that had unclear senantic 2 oes or
What I find striking 1s th these ere just the veariebdles

"ranch sentence--a language
of serial pos one per-
es

C
fectly. Of course, wh
Tm tablished effects in

eaningfulness el
ments on immediave memory.

 

ough an aspect

“I propose to introduce telepraphic i
the variables
Ss

¢

D

of LEARNMORE called BADEAR. The BADEAR program will simulate

of stress, meaning fulness, and serial position in orovidings LAS with a depleted
version of the sentence. The locus of the effect of BADEAR will be between
boxes 4 and 8 in the flowchart of Figure 2. Basically it will not bass all
words onto BUILDPATH. Rether some words will "slip fron consciousness” after
failing to be persed. It will tend to omit words wher: (a) they are unstressed,
(ob) their meaning is not known, (c) a critical nusoer of new words in the
sentence nave already been pas ssed to BULLDPATH. I suspect this critical number

is something Like one or two.

Factors (2) and (b) would generate che effects o
Factor (c) would yield good memory Tor the fir

good memory children do show Hr last wor
term acoustic memory.

7
oi

63
    
  

   
 

An interesting fez @ B at, expanded, LAS
sould tbe able to receive more of the sentence. tons and imita-
sions would grow as Goes a child's. This would be colicit mechanisn
for an ides suggestes ty Braine (1971), Olson (1973, Tnducing 2
grammar Drom derenerate sentences presents an intere How is it tnat

pangon its rules for generating te eecn? verely
ed 4 r oR fuller sé does not follow
S$ are W oe le means for
expressing tne same tho hanisa incorporated
that will strengenen some & mat lative Rules to be
would be t essTul FUDERSTAND and
uccessfully ignt. tt & ares ont of
parsing netw ack ¢ eir relative
Subjects wo of a sta i Tneffective
ne originel rc oO a on word utterances would descend
4 of the stack and so become unavailable. This strengtn mechnanisn
the same as used to order Links in the HAM memory model. This is a different
way to bring formation to vear in grammer induction than thac bron
posed for rather than seexing explicit éisconfircation of rules,
the rules ned out of existence as more adequate rules taxe

 

Ss
over the used to occupy in sentence understending and

af *
eneravicn.

 

with the following form:

 

START

NP é,

This grammar requires considerable backup if the sentence does. not have an RA
relation. As suggested earlier it would be more efficient if LAS were given the
power to transform the grammar into the following form:
STOP
GRA
iP NP

E&.2RB
a
STOP

Given that there are s ous time problems (see introduction of proposal)
in parsing, it isc i 4 methods be incorporated in the learning program
for optinizing the grammar. The merging of arcs, besides making the grammar

more efficient, would be another form of generalization. It could be used to
further merge and build up word classes.

6h
 

Any a errr ok aranhkinn ar aoe s, reyin ore
tyo further ways thab semantics can be used to aid language
+ ~ an 1 4

 

7
h contain color names. Curren
nt
wu

ae

u
the amoun overlay between the mem
er its over é

ie property.

joe

oO ics would be to lesse s

t ations of sentences. I¢ should somet 5
se interpretations. For instance, suppose @ sentence can

din. Because of the conceptue constraint t
to guess their connection. This use of conceptu
domain could also be used py UNDERSTAND to 3d t
She model of the Schank's (1972) system. Tnat
tandl cing @ sentence by use of syntactic information, i
c traints to predict what a interpretat
sdietion can then be checked for synvac
ammar. It would be profitable to try t
ike Schank's within the rigors of the

eb 0
Oo mY ct or
o
cr
ry
a

O

O ct
3

as

jQ v
ee

3

cr

uy

rae

9

sy ob

1)
cr
ot
m
n
mp
E
fp
ct
foe
RO

Ww
ry
nm
ct
wm

wy
my
ry UD ts
fe om
OM DD Ps
a fe
rp op oO
mPct 1
@ ct rs ch OW ocr
Q

@ ct hy
0

mM
won
a

i

by use of the network
dictive persing system

PAymal
formalisms,

 

A Procedural Semantics
So far LAS hes been principally concerned with representing the meaning
conveyed by a declarative sentence. However, language hes other purposes than

er commands

oO
just to communicate meanings from one speaker to enoener Co dé
ly in the box,
D
a

end questions. For instance, consider the sentence Put the do
Currently, UNDERSTAND might retrieve the sentence'ts meaning as S
of LAS that it out. the dolly in the box. This is the dec Laratiy
However, in addition LAS should evoke an action that

an ection to decide whether to comoly. Al

21 meaning of the sentence. The procedural meaning of decl:
$ very Simple: store this sentence. This is already as
ment of the sentence. However, the procedural meanings underlying

‘o

 

 

uw
3

0D
ry

Pa
r
r

cr Dp
HO
oom
@.
Nn
ct
c
ri
D

Oo «MM
oO

“s

bry WO ck
F4 rs

A

uv

ou

fy

0

 

1 ry
Om
e
'

§
Wy ts

0
ocr
0

aw ocr
3
-
ra)

yo mw
ct
ae

3

eat

pes of sentences are more complex. A large part of the success of

's system is that it.was adequately able to deal with the procedural
of various sentences!’ semantics. It is important that LAS begin to
tnese too. .

4
Me

m =
oO nF
Pe iS $3
-J v 2
<
eo
ck
ry

He

~

0

dpom
(9

this would mean, in terms of LAS's network grammars, is enrich
set of tions vat can be stored. Currently, the only actions are ones
result in the creation of pieces of HAM structure, i.e., : i

LAS will have to ene other internal actions that svecify whet it does
the declarative knowledge. These will include commands to answer the qu
or obey the order. HAM already has commands that direct it to answer aq
but executing orders would be something new. As part - the HAM project,
working on methods for incorporating procedural knowledge into a network
tem. It is unclear yet what success I will have here.

cr ocr

ove
(D

Q

j4
io

H
3
ci

}
<
m
ree
by
9
£

jos bck

Mw
Ug
>

x
Pa
ct

oO
u

0

Ln
fet ch ocr
O
a)

poe fee
°
un

HS

a

ta

u
!

65
a

Andersoa

language whose semantics
n a r Consider for
ch Pinite article--the ab
bjeck whi: > listener
t

 

 

Li s be i

This partic ly g is to ¥y

to speaker and context. Since the referent or you completely chanzes with

speaker, a child would be lost if he tried to associate its meaning with some
f a at it as having as meaning @ pro-

HAM memory-node. He must be pregared to tre
cedure for determining the referent.

Provided that LAS has the facilities for representing and evaluating pro-
cedures, there seem no difficulties in learning those aspects of language
which are heavily embued with procedural seransies. Language learning wi
tinue to arise from pairing sentences with secentic interpretations. Howeve
serantic interpretetions will now contein & procedural as well as a declarat
aspect. Again language learning will consis learning mappings betweea s
tences and the now-enriched semantic represenvations.

Experimentation

 

As stated before, I do not think that i
the principal focus of the project. There 11 much further research that
needs to be done in the way of specifying elgorithn that are capadle of language
induction. Nonetheless, in parallel with shis research, I would like to perform
experiments to get some initial assessments of the viability of the proposed
elgorithms. The type of information relevant to evaluating LAS is only acquired
by looking at artivical languages. With these artificial languages it is possible
to test LAS's predictions about language learnability and generalization.

mental research should yet be

Criticisms of Experiments with Artificial Languages

For ethical reasons it is not possible to expose young children, just
learning their first language, to an artificial language which LAS had identi-
fied es degenerate and probably not learnable. This means that all experimen-
tation with artificial languages must be Gone on older children already vell-
established in their first language or on aaylts, Conseauently, the first lan-
guage may be mediating acauisition of the second language. ‘There is evidence
(see Lennenoerg, 1967) that there is a critical initial period during which
languages can be learned much more succéessiu.y than in later years. Lennenberg
speculates that there is a pirysiological basis for this critical period. ‘Thus,
one might wonder whether the same processes are peing studied with older sub-
jects as in the young child. Personally, i o doubt that the mechanisms of
language-acquisition are the entirely same wi

ct

.

s
h the young child in first language
learning as With the older subject in second language Learning. However, it does

cr
Oo
 
  

      
 

   

 

Other criticisms (e.g., those of signin, 971; Milter, 1967) of stucies
re 7 1a arvnoin n the fact that tnese Languages are a
é molicated t an artificial labora-
j lex functions; the
ech. LOW a"
GACL
1 phenomena. Another
ose studies of
5 a semantic referent.
Clearly, 5: 3 f: of algoritans @ subject
can employ. neuristics used bY LAS would be useless without senan-
tics. (1972, 1973) neve shown that the existeace or a
seman uge effect on Language acquisition. Except for control
‘condi iments will involve @ seransic referent

  
  

Languege Learneboility

2 oS

on elgorithm is that the graph defo mation condi-
tion ts ms ation between the surface structure of tne sen-
tence and the sal structure. These is, the surface structure mist
preserve the original connectivity of concepts. In Section A5 we described
languages which violated this assumption. Consider the following language:
oS at Bucs

S$ > NP NP relation

HP > noun (Color) (adjective) {clause )
CLAUSE >» te NP relation

NOUN + square, circle, triangle, diamond
Color > red, blue

Size > small, large

Relation > above, pelow, right-of, Laft-of

cS

This is en expanded version of GRAMMAR] described in Table 1. (The element te
a

in
An

serves the function of a relative pronoun like that.) An example of a sentence
a

Loy
this languege is Squere red te triangle pig above circle Dive small right-of.

experinent Twill Go compares Four conditions of jearning for this langucge-

 

 

 

No reference. Here subjects simply study strings of the language trying to
infer their grammatical structure,

Bad semantics. Here & picture of the sentence's referent will be presentea
elong with the sentences. However, the relatioaship between the sentence'’s
semantic referent and the surface structure will violate LAS's constraints.
The adjective associated uth the ith noun phrase will modify the (n+ 1- iL}
shape in the. sentence (where n is the number of noun phrases). For example,
the adjectives associated with the first noun phrase will modify the last

6T
om
yw
~~

 

S

(oo)

 

 

 

 

 

 

 

 

 

 

(b)
( RED
SY
(c)

 

Figure 19. Different

semantic
the same
triangle
right-of.

roferents for the
sentencees Square red te
pig above circle blue 5m

ay

   
  
 

 

 
7 ft yet -
Anderson

     
 

shape. Similarly

(q + 2 - ijth rel

So for instence the

petyeen the first pai triangle. Gne ay in
picture for the example sentence 15 given in Fiuuce 19a.

ud)

 

 

h, Good semantics plus main oroposition. The picture in this condition wiil
be the same as in 3 but the two shapes in the main proposition will be
highlignted. In this cond@ition LAS would be guaranteed of successfully
bracketing the sentence because the main proposition is given.

In some ways this experiment is Like Moesser and Bregman's, However, here

English words are used s0 that the subjects do not need to induce the language's

j its grammar. Fob corresponds to the situation faced

1 5
sh words were replaced by nonsense syllables this would
tion of the Language to make

pLiti induction tractisle. Tne |
predictions of LAS are, of course, that best learning occurs in Condition 4,
next best in 3, and failure of any learning in 1 and 2. It would not be sur-
prising ta see gunjects perform better in Ltren in 2 since in they might par-

7 ~ 77 * : a cute ce eee ok peace kDa
BLL Ve BULB ti Ch BO PL OPE Bae ew decent theo
> ve

The procedure would have subjects in all conditions study the same sequence
of sentences but vary the accompanying semantic information according to condi-
tion. After a study phase they would be tested for grammaticelity judgments
about a set of sentences, Some of which violate one of the rules for generation.
Since the syntax of the language is the sane in all four condivions, the sane
sentences will be eramnat 1 in all four conditions. @yen though the synvtac-
tic information given d study will be the same in all conditions, marked
@ifferences in syntacti Tr

r 52
+

Ss

He ete (9
39

oo 2

ao
ct
uw 0

wledge should appear across conditions. ine

guences of study trials with sequences of test
tudy six sentences, with the semantic information
jate to his condition, if any). Then he would see six test pairs, one

ce of each pair violating some syntactic rule. For each pair of he would
o- choose the grammatically correct pair. By frequently alternating study
st, 44 would be possible to carefully monitor the growth of information
in the conditions.

Many readers may not be surprised by the prediction of petter learning in
Conditions 3 and hk. Hopefully, the significance of such an outcome would be
clear. It would snow shat semantics is impo tant to induction of the
structure of a natural language. Hovever, i

ck
o
(b

would also show that semantics

is useless if the relation between the semantic referent and the syntactic
structure is arbitrary. The surface structure of the sentence must be a praph-
deformation of the underlying semantic structure. Failures to eppreciate the
contribution of semantics to Language induction and failure to understand the
nature of this contribution of senanties to the induction process nave been
fundamental in the stagnation of attempts to understand the algorithms permitting
Janaguage induction. These facts may be obvious woen pointed one but they have
. . . wate . . rd > - = D2 Phe ar wa
been unavailable to the Linguistic theorists 19F fifteen years.

he same purpS

ea

S
hat is, they ¢
A

 

3
ssibl so that the target

language can be identified. However, ainc ed by LAS are not tre
came ag those suggested by Chomsky. For insvance, Chomsky proposed that vrans—~
formations which reversed the order of words in 4& sentence would be unaccentanle.
Tris is because such 2 rule does not refer to tne santence's constituent struc-
tore. However, 2 languesge which contained sentences of a natural language and
their reversals would be learnable by LAS. Te would just develop one seu or
rules for sentences in one order and another indesendent set for reverse order
sentences. It would be interesting to see whether numan subjects could iearn

such a language.

In the example of the induction of GRAGIARI we found that tne
for LAS to detect non-senantic contingencies between syntactic cno
a °

first noun-phrase end tn the second noun-phrase pushed to in the. main network.
Wor instance, it is possible that a morphenic emoellishment of the a jectives
i ; hrase may depend on & choice of morphemie embe

the noun in the first noun phrase. Human subjects should also find it hard
to detect such syntactic contingencies.

 

 

oO
m

There ar nother set of predictions, besides those concerned with language
learnability, waic + will be useful to explore. LAS makes predictions about
the situations under whieh humans will ten to generalize rules end when humans
will not. Suppose LAS learned the following gremmar: ,

S$ > VERB WP NP

we > (PREPP) Wy, (ADS)
PREPP > PREP Ne

Ny > boy, girl, ete.
No > room, bank, etc.
ADJ + tall, nice, etc.
PREP > in, near, etc.
VERB > like, nit, etc.

1
+

 

 

A typical sentence in this language would be Like

 

 

which means The tall boy in the room likes the nice 83 1. Tnis lang e is
given English terms only to maxe its semanties Clearer. Suppose, in fact, words
in the language were das meaning man, ji> meaning wonar, Fos meaning boy, and

3
tuk meaning girl. Suppose the subject studies the following pair of sentences!

1. Like das tuk.
2, Like fos jir.

10
 

      
   
   
  

Then, it is interesving to consider his judgmenes of the nacceptaoility of
sentences Like:
3, Like das tuk.
4. Like das jir.
5S, Like jir Gas.
Accept involves recalling senteace (1), but
nVOLV c LAS would currently mexe th

3

'
’

cy fa
wv

ip
uy)

  

oa _- a “~
Oe wONeECNE 3, 5325
oo 5 mS 7

of their semantic similarity

\ s. ‘Tne words 3:
could, for Dax 4iff 5 cane inflection wnen they apr

ferent
t when pr

 

ion in this artificial
Jangu Would he accent
senven

6. Like in room boy tall girl

7. Like girl in room boy tall
That is, will rules gener alize from the subject noun purase to the object noun
phrase. As LAS is curren ntly constituted such gener rai tzasson s would not occur
until it hed built up fairly stable now pnrases. Again suppose LAS had initially
only encounterecé simple sentences suen es (8):

uch as (8) LAS would learn the class of nouns that Go

From sentences Ss e

first and second noun phrase slots. Suppose then sencence (9) was studied. On
the basis of it, would senvence (10) be accepted as grammatical? That is, would
the preposi itional phrase in bank generalize to Ov “near nouns in the same class as
woman?

9, Like boy in bank women
10. Like girl in bank man

This would be am example of right zgenereli
In contrast, LAS does perform left generali
LAS would accept (12).

zation which does not occur in LAS.
zation. That is, after studying {11}

lL. Like boy woman nice
12, Like boy man nice

fi
 

   
 

poses, one concerned with psycnology and one
ence. IL think this mixed purpose is fruic-
réilization of ideas from two fields and so
on. There is no gueranvee that LAS, in the
will ever achleve the goal of an adequate
acauisition of language. However, 2 certain outcome
er understanding of the information-processing demands
and of the role of a semantic referent in gremmar in-
“re Will learn wnet is wrong with one explicit set of
i | =ven that would be 2a significant contribution to the
Currie UbEUre be cas Gevelupmeub dn a PlelG rich in Gave Dub abmvel, LuLaLiy
Jecking explicic information-processing theories. I hope, of course, that the
processes uncovered in the LAS project wiil be the same as those used bY
humans in language learning. A successful simulation program would constitute
an enormous advance in our understanding of cognitive development.

The contributions of LAS to the artificial intelligence field are less
certain and more distant. Nonetheless, generality in language understanding
systems is an important goal and one for which a learning system approach
seens ideal. It is therefore importent to understand the contribution language
learning systems can make in this field. It yould be a significant advance to
know in detail way & learning system approach was not the answer to language
understanding or at least why LAS was not the right sort of learning systen.

Of course, if LAS does prove to be the basis for a viable language understanding
system, its contribution to artificial intelligence will also pe of considerable
importance. ,

FE, Fecilities Available

 

{ shall neve evailable the entire facili
Center, University of Michigan. My current @
but can be extended for one to three years. My pr
Michigan Terminal System which supports a rich vari
the programming will be performed in Michigan LISP (
which is e relatively economical and an error-free ve

c

ty of programs. Most of
S £ % Wilcox, 197%)
LISP.

{2
mag ae son

    
  
 

ALPAC (Automated Languag

Language and
machines :

te ; >t
National

 

 

 

Scieaces, Washiagton, 232
Andersoa, J. R. Computer simulation of a language-acquisitioa system in
R, L Solso (&d.) Information Processing and Cognition: The Loyola Svmoostun.

 

 

 

 

Washington: Lawrence Exlbaun, 19/5.

Anderson, J. &. and: Bower, G. H. -Human associative menory. Washington:
Winston and Sons, 1973.

 

 

 

 

Bar-Hiliel, Y. Language aad information. Reading, Mass: Addison-Wesley, 1964.

ns s In J. R. Hayes
opment of languaze. New York: Wiley, 1970.

Bever, T. G.. The cog
nt

itive basis for lingulstic structure
(Ed.) Cogn t vel

ks

wv

| od

Q

ban

9
overs!

 

 

Bierman, A. W. An interactive finite-state language learner. First USA-JAPAN
Computer Conference, 1972.

Bierman, A. W. and Feldman, J. A. A survey of resuits in gramnatical inference.
In (£d.), Frontiers of pattern recoenition. New York: Academic
Press, 1972.

blasaeil, K. and yensen, rv. stress and word position as detetminants of
induction in first-language learners. Journal cf Speech and Hearing
Research, 1970, 13, 193-202. .

 

Bloon, L. One word at a time. The Hague: Mouton, 1973.

Bobrow, D. G. A question~answering system for high school algebra, word
problems. AFIPS Conference Proceedings, 1964, 26, 577-589.

 

 

Bowerman, M. Early syntactic development. Cambridge, England: University
Press, 1973.

Boyer, R. S. Loc!

king: A restriction of resolution. Ph.D. Dissertation.
University of Texa

xas at Austin, 1971.

Braine, M. D. S. On learning grammatical order ox words. Psychological
Review, 1963, 70, 323-348.

Braine, M. D. S. On two types of models of the internalization of grammars.
In D. J. Sloben (Ed.) The ontogenesis of grammar. New York: Academic Press,

1971, 153-188.

 
 

Broan, FP. vhe verbal enviroauent of the language-Learaing child. boaogra hg

P
of the American Speech & Hearing Association, 1972, 1?.

rT eee We ee ae

er rr cs ae

 

 

Brown, R. A first language. Cambridge, Masse: larvard

Brown, R. and Fraser, ©

The acquisitioa of syntax. im c. N. Cofe
B. S, Musgrave (Eds °
{

+
&

, Yarbal pehavior and jearnmings Problens and processes.

Vetoes oo 2 Q jearning: Problens 2n8 8 -

New York: MeGraw-Hil

Bruner, J. 5-, Coodnow, J., and Austin, G- Ae A study of thinking. hew York:
Wiley, 1956.

Charniak, E. Computer solution of word problems. proceedings of the Intec
national Joint Conference of Arcificial Tarelligence- Wasnington, D- C.3
1969, 303-316-

Chomsky, N. Aspects of the theory of syntax: Cambridge, Mass-:; MIT Press, 1955.
Colby, K. Me and Enea, H. Inductive inference by intelligent machines.
Scientia, 103, 669-720 (Jan. 7 Feb. 1968).

Clark, E. Vv. Non-Linguistic strategies and the acquisition of word meanings.

Cogaition: International Journal of Cogaitive Psycholosy, 1974, in press.

Coles, L. S- Talking with a robot in Eng
~narional Joiat Conference on Artifict:

1969, 587-596. ~ —

Proceedings of the Inter-

elligence.- Washington, D. ©.

f3
ee
r

3
rr?

 

uisition of precedence grammars.
Engineering and Applied Science,
es, 1970.

Crespi-Reghizzi, S. The mechanical acq
Report No. UCLA-ENG-7054, School of
University of California at Los Angel

Dreyfus, H. L. What computers can't do. New York: Harper and Row, 1972.

RO

Ervin, S. M. Imitation and structural change ina children's Language.
In E. H. Lennenberg (Ed.), New directions in the study_of Language.
Cambridge, Mass-: MIT Press, 1964, 163-159.

Feldman, J. A. Sone decidability results on g

ramnatical inference and complexity.
A. Lt. memo No 93.1, Computer Science Department,

Stanford University, 1970.

Fergusen, C. A., Perzer, D. B., & Weeks, T. E. Model-and-replica phonological
grammar of a child's first words. Lingua, 1973, 31, 35-55.

of syntax. Paper presented

Fernald, C- Children’s active and passive knowleds
to the Midwestern Psychological Association,

197

on

Fikes, R. E., Hart, p. £. and Nilsson, WN. OJ- Some new directions in robot
problem solving. Stanford Research Institute, August, 1972.

74
Fhllmore, C. J. The casa for case. in E. Bach and R. J. Harus (Eds.),
+

 

     
     
 

 

 

Universals in tinenistic theory. New York: Holz, Rinehart anc

Hiaston, 190d.
Friedmac, J. A computer mod American Elsevier,1971.
Praser, D., Bellugi, U.,

hension, and projection. Jour

1963, 2, 121-135.

Research on intelligent question answering
che ACM 23rd National Conference. Princeton:

 

 

 

 

, 169~i8l.

Hafner, C. and Wilcox, B. LIS? MTS programmer's manual. tent
Research Communication 302 and Information Processing Paper
¥y 1974
a

Tim wnt oot nhac
University or “icnigar.,

de atical initerence. Technical Report No. CS 139,
Computer Science Department, Stanford University, August, 1969.

uary 1966. Technical Repor
Naval Pasearch, Information Systems Branch.
Reliey, . L- hachy syucecclé ACQULSAL LU. E$<-3729, Lhe Rand CoLye, baka
Monica, California, 1907.

Kelloggs, C. H. A natural language compiler for on-line data management.
Proceedings of the 1965 Fall Joint Computer Conference, 473-492.

 

 

 

Kuno, S. The predictive analyzer and a path elimination technique. Communications
of the ACM, 1965, 7, 453-462.

uage without ability to speak: A case report.

Lenneberg, E. H. Understand
ychology, 1962, 65, A19-425.

i
Journal of Abnormal and Sozia

Ww a9

Lenneberg, E. H. Biolowical foundations of language. New York: Wiley, 1967.

Lindsay, R. K. Inferential memory aS a basis of machines which understand
natural language. In E. A. Feigenbaum and J. Faldman (Eds.), Computers
and thought. New York: McGraw-Hill, 1963.

Loveland, D. W. A linear format for resolution. Proceedings of the IRIA
Symposium on Automatic Demonstration. New York: Springer-Varlay,

1970, 147-162. .

 

 

 

 

 

Luckham, D. Reitinemants in resolution theory. Proceedings of the IRIA
Symposium on Automatic Demonstration. New York: Springer-Varlay,

1970, 163-190.

75
Miller, G. A. Thea pss chology of comeunication, New York: Basic Books, 196

Minsky, M. CEd.), 5 Semantic information orocessing. Cambridge, Mass.: HIT
Press, 1958

 

 

 

 

 

Moeser, S. D. and Bregman, A. 5

of Verbal Learning aad. Verb:

 

 

Moore, E. F. Gedanken experiments on sequeatial machines. Automata Studies,
Princeton, 1956.

Olson, G. M. Developmental changes in memory and the acquisition of language.
In T. E. Moore (Ed.), Cognitive development and the acquisition of language.
New York: Academié Press, 1973.

Pao, T. W. L. A solution of the syntactic i nduction-conference problea for
3 non-trivial subset context-tree languages. Report } No. 70-19, The ,

see School of Electrical Eagineering, University of Pennsylvania,

slat, 1959.

 

 

Guillian, M. R. The teachable language comprehender. Communications of
the Association for Computing Machinery, 1969, 12, 459-476.

 

Reber, A. S. Transfer of structure in synthetic languages. Journal of
Experimental Psychology, 1969, 81, 115-119.

 

Richards, I. A., Jasuilko, E. and Gibson, C. Russian through pictures,
Book I. New York: Washington Square Press, 1961.

Robinson, J. A. A machine-oriented logic based on the resolution principle.
Journal of the ACM, 1965, 12, 23-41.

Rumelhart, D. E., Lindsay, P, and Norman, p. A. A process model for long-
term memory. In E. Tulving and W. Donaldson (Eds.), Organization of
memory. . New York: Academic Press, 1972.

Saporta, S., Blumenthal, A. L., Lackowski, P. and Reiff, D. G. Grammatical
models of language learning. In R. J. DePietro (Ed.), Monograph Series
on language and Linguistics, Vol. 16. Report of the 14th Annual Round
Table Meeting on Linguistic aad Language Studies, 1963, 133-142.

 

 

 

 

 

 

Schank, R. C. Conceptual dependency: A theory of natural-language understanding.
Cognitive Psychology, 1972, 3, 552-631. ,

Schlesinger, I. M. . Production of utterances and language acquisition.
In D. I. Slobin (Ed.), The ontogenesis of grammar. New York: Academic
Press, 1971.

 

76
 

Scholes, R. J. The role of grammaticality fin the imitation of wore s
by children and aduits. Journal of Yerbal Learning anc ;
1969, 8, 225-228.

 

 

 

 

Scholes, R. J. On functors and conten ntives in childzena 's imbtations of
word stcings. Journal of Verbal Learning and Verbal Behavior 1979,
9, 167-170.

Sehwarez, R. M., B rgex, J. F. and Simmons, R. F. A deductive questioa~
answerer for natural langus is

atio
Association for Computing Machinery, 1970, 13, 167-133.

sd

 

 

Shamir, E. and Bar-Hillel, Y. Review 2476. Computing Raviews, 1962, 3, 5.
Siklossy, L. A Language~Leatning ,euristic progran. Cosnitive Psycnoioay,

a tai
1971, 2, 479-495.

 

 

 

 

 

 

 

 

 

Simmons, R. F. Natural language question- answer ring systems: 19909.
Communications of the Asso ciation for Computing Machinery, 1970, 13, 15-30.
Simmons, R. F. Semantic ft networks: Their computations and use for understanding
English sentences. In RB. C. Schank and K. M Colby (Eds.), Comput ar
models of chought end language. San FY neisco: Freeman, 1973.
> -ce le 3 ad rt GS
Simon, H. A The sciences of the artificial. Cazbricge, Mass.: MIT Pressy/76 7.

 

 

oe acquisitioa and cognitive development. In
tive development end tne acquisition of language.

 

 

 

 

 

=s, 1973.
Slagle, J. R. Experiments with a deductive question-answering program.
Communications of the As sociation for Computing Machinery, 1965,

 

8, 792-798.

Slagle, J. R. Automatic theorem proving with renamable and senantic resolution.

Journal of the ACM, 1967, 14, 687-697.

Siobin, D. I. The ontogenesis of grammar. New York: Academic Press, 1971.

 

 

 

Solomonoff, R.- J. A fornal theory of inductive inference, Part It. Information
and Control, 1964, 7, 224-254.

 

Weizenbaum, J. ELIZA - a computer program for the study of natural Language
communications between man and machine. Comzunicetions of the ACM,
1966, 1, 36-45.

Wilks, Y. The Stanford MI and understanding PIO} ect. In Senank and Colby
(Eds.), Computer models of thought _and language. San Francisco, 1973.

 

Winograd, T. Understanding natural language. Cognitive Psychology, 1972,
3, 1-191.

77
Anderson

 

 

 

Winston, P.-H. Learnin ng Structural descriptions fron examples. MIT
Artificial Intellisenc ce Laboratory Project AI-TR -~23i, L970.

Woods, W. A, Procedural semantics for a question-answering machine.
Proceedings ofthe 1958 Fall Joint Comsurer Conference, 457-471,

atural languaze understanding: An application

woods, W. A. Progress inn
FLIPS Proceedings, 1973 National Computer Gonftorence

to lunar geology. AF
and Exposition.

 

 

Woods, W. A. Transition network grammars for natural language analysis,
Communications of tha ACM, 1970, 13, 591-606.

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