92

Seo

C
P pf \8
\ fo >

  
 
  

*COLOR {

   

J

CIRCLE #OA RED SQUARE Hig | BLUE

 

Figure 4. An example of a HAM structure encoding both categorical information
and word class information a
(Ideate red 1)
(Ideate square 2)
(Ideate above 3)
(Ideate circle hy
(Out-of KL)
(out-of X 2)
(Out-of X 8)
(Oojectify & Y)
(Relatify 8°3)
(Out-oF Y

 

t ply to any m2

st languages will b
will also be used to illustrate the SPEAX anc UNDER
eribed shortly. The first, GRAMMARL, is a simple artificial grammer. Ta
second, GRAMMAR, is a more complex gramzar Tor @ Su

aYnAAD

D
aefined by tne rewrite rules in Table l. GRAMMARL wa
mally different fron Englisn word order. Tne sentenc
be read as asserting the first noun-phrese nas t
last word to the second moun phrase. For purpose il

of these languages are English but they need nov oe. GRAMMAR] 1s 4 finite
Language without recurssicn. In contrast, in GRAL-MARe the NP element has an

amb dann t OTATOS eratnh naam manwnndiareadear aot) MD  oeamarctine 2 nntoential infin-

Sp eee Se ee MOCUPILV Ry mem y peor cv. meen et

constructions.

we

a

ite embedding of

uy
fo

3
fp

In both gremmers, it is assumed that above end below are connected to the
idea as are right-of and left-of. Tne words differ in the assigment of their
NP arguments to subject and object roles- Tnus the difference between the
word pairs is syntactic This is indicated by having the words pelong to
two word classes RA and RB. Thus, UNDERSTAND with CRANMAR2 would derive the
same HAM representation in Figure 3 for the sentences The red square is evo0ve
the circle and The circle is below the rea square. It yould have been pos
sible to generate distinct representations For these two sentences. I think
this would have Deen less psycholegicetly interesting. Basically, the network
ise

 

grangar makes the inferences that A below 3 quivalent to B above A and en-

codes the latter.

 

TABLE 1

The Two Test Grammars

GRAMMARL GRAMMAR

Ss + WP NP RA Ss: +> WNP is ADJ

NP NP RB NP is RA Ne
NP + SHAPE (COLOR) (SIZE) WP is RB NP
SHAPE + square, circle, et. NP + (the,a) NP* CLAUSE»
COLOR + red, blue, etc. . ye* +» SHAPE
SIZE > large, small, etc. . + ADI SHAPE
RA- > above, right-of CLAUSE > that is ADI

that is RA NP

27
 

TART? 4 abbas
TABLE 2 continued

g +» below, left-ot CLAUSE + thet is RB uP

SHAPE + square, circle, euc.

ADS + red, bis, blue, ebc.

RA + above, right-of

RB + below, left-of

Figure 5 illustrates the parsing netuorss for the grammars. It should
be understood that thes networks have been deliberately written in an inefri-
cient manner. For instence, note in CRAVMARL thet there are tyo distincs patns
in the main START network. Tae first is for tnose sentences viva RA relations
and the second for tnose sentences with 2B relations. If a sentence input
to UNDERSTAID nas a RB relavion, UNDERSTAND will first attempe to parse it by
the first branch. The tyvo noun phrase branches will succeed bus the relation
branch will fail. UNDERSTAND will have to back-up and try the second branc
that leads to 23. This costly back-up 25 not really necessary. It would have
been possible to have constructed the START network in the following form:
STGP
NP HP aT

 

not branch until the critical re
until une e&
1e reoresentati

  

Table 2 provides a formal specification of the information stored in LAS's
network grammars. A node either hes a number of arcs proce eeding out of it
(1a) or it is a stop node (1b). In speaking end vaderatandins LAS will try to
find some path through the network ending with a stop noce. Each are consists
of some condition that must be true of the sen z to be ed
in parsing (under rstanding) the sentence. Tn t
be taken if the condition is met. This acti
conceptual structure to correspond to the m

thet point. Finally, en are includes speci
control should transfer after performing the
zero or more HAM memory commands (rule 3).
or more memory commands also (rule ba). These

e true of the incoming word. Alternatively,
push to an embedded network (rule hb). For instanc

 

 

in Figure 3 were to be spo: ken using CRAMMARL. Tae START ne be
called to realize the X_is above ¥ proposition. The erpedded NP netvork would
be called to realize the ¥ is red and X ls scuare propositions. in pushing
to a network two things must be specified-- “NODE, raich is the embedded net-
work and VAR, wnaich is the memory node at waich the main end emoecded propo-
sitions jntersect. The element t is rule ib is 2 plsce-hnolder for invormation
that is needed vy the control mechanisms of the UNDERSTAND progran. The
three rules 6a, 6b, and 6c specify three types of arguments thay memory
commands can have. They can either directly refer to mexory nodes, or refer
to the current word in the sentence, oF refer to varieble: c} 3
Ms workoa far Ammar te
Ai ko for Cconmmanl 7

 

Networks for CRAMMARD a

 

 

 

 

 

NP 2 COP  € AdT
START awenetiemnenntom SZ seSh tom STOP
NP = RA
NP
7 OSSD G errno ST OP
4 2 COD = RB . NP
Ns3 = CO! 20 SJ aannammeemtioe SB mm 2STOP
enem ae
NP — se Nl —s STOP
€ ©SHAPE CLAUSE
NPL > AL— - to STOP
NPL
AD ceeeenrnnrernennrentnnntonnsto TD
& REL = cop

 

CLAUSE CL

 

Figure 5. The natwork grammars used by BAS

29
ry
0
QO
ry
rs
Cc
fu
Q
Lee)
be
rs
ct
Ly
o
QO
G
ion
by
wv
oD
©
bh
tS
J
Ky
oO
rs
C9

NODE > ARCS (1a)
> stop (1b)
ARC > CONDETION ACTION NODE {2)

 

ACTION > OMMAND* 3)
CONDITION > (COMMAND® } (he)
> + NODS (uD)
COMMAND > aG ARG (5)
ARG id menory node 62
x

‘
oO
et Net Nee

 

> (6
+ Ady xh, KS (Se
FUNCTION > , oojectify, relatify, ideate (7)

Mable 3 provides the ancoding of the nebyork for GR

   

  
 
 

Note that there tencas to be a l-
and LAS networ: Tt each network expresses just
calls one ; to exoress
dence is not quilt: Tt in GRAMHARL or G@

no Lave necessalasy © pore ce

tures to commend then. SPEAK
L:

These grammar networks have a2 number of a
rc mtence comprehension and generaclon.
7?

and UNDERSTAND use the same network for sent
Thus, LAS is the first extent system to have & uniform gremmetica notation for
jts parsing and generation systems. in this way, LAS hes only to induce ons
set of grommatical rules to do poth tasks- Such networks are nodular in two
senses. First, they are relatively indepencent of each otner. Secona, tney
are independent of the SPEAK and UNDERSTAND rrograms snav use then. Thais
modularity greatly simplifies LAS's tasx 0 induction. LAS cnly induce

maa

r gr Poaz 3
the network grammars; the interpretative SPEAK and UNDERSTAND programs repre
i lve x

sent innate

r

inguistic competences. Finally, the networks themse
very simple with. limited conditions and actions. Tous, LAS need consider
only a small range of possibilities in inducing 4 network. Tae n=
salism gains its expressive power by tne embedding of networks. Hec

network modularity, the induction task does not incresse with the complexity
of embedding.

fee

Tt might be questioned whether it is really 2a virtue to have the same
representation for the grammatical knowleage both for unders
a +5 :

duction. It is 4 com=on ooservation that children's ability to uncer cand
sentences precedes their ability +o generate sentences. LAs would noe seen
to be able to simulate this basic fact of languese iearning. However, there
may be reasons way child production does not mirror comnprenension otner than
that different grammatical competences underlie the two. The child rey not

yet have acquired the physical mastery to produce cartel b
is the case, for insvance, with Lenneberg's (1962) enarthric cnila wno under-

30
Tha eontruetion of CRAMMARL

qPpuT aoEErKUP TNSUBK CORT epuT *SuBRd}

A

? {PRKCON

4 corr PRP START PATH

“e CELPUSH AL TONP) ({QUT-UF Al X5}) S2)

5 Ci PUSH XL T Npy £f08JeECTIF %5 X1l)) S4 }))

6 (DEEPROP S2 PATH

i CE CPUSH Ke T NPQ (AOUGETCTIFY x5 xX2)3 S3 y)}

8 > (WEF PROP 33 PATA s
3 QE CEPOFATE FORD X¥4) (GOUT-OF WORD #QA)) (CRELATIFY 45 Ad) stop
LO Coir enOP S4& PATH ee

Li (UE PUSH X2 T NP) £(OQUT-DF %¥2 X5)) S5 423

L2 LDEEPRUP $5 PATH

13 LE UCLOFATE WORD %G}) LOUT-GF WORD HRA)) (ARELATIFY X5 X43) STOP ?
io LDEFPROP NP PATH :
15 COC CTUEATE WORD X43} ({GUT-OF A4 a SHAPE) ) {(OUT-OF Al X43) NPZ 7;
17 {OCFPROP NP2 PATH

is (CUPUSH Xl T COLGR) NIL NPS }

13 C NTL SIL NP3))}
20 {NFFPROP NPB PATH

2i CO¢PUSH X11 T SIZE} NIL STOP 3}
22 {NIL NIL STOP ))}
?3 (DEF PROP COLGR PATH

2% CLL CEDEATE WGRD %4) {QUT-UF X4 *CULOR)} ({QUT-OF Xi X49) STOP
29 (NEF PROP SIZE PATH

26 . CELL IDEATE WORD X42). (OUT-UF X* aS1ZE)3 @(OUT-DF ¥1 X43) STUD }
2t (TALK) -
2d ({IDEATE SQUARE XLICTCEATE CIRCLE X23

29 C(UUT-UF AL *SHAPEV(OUT-OF 2 *SHAPE}) }

30 (CIDEATE REO XB) CTOCATE GREEN %4))

31 ((NUT-OF X%3 COLOR) (OUT-OF X4% #COLGR))

42 CLISP SETO X1 NIL)

33 CCIDEATE SMALL X5) (1 DEATE LARGE X1)})

34 ({UUT-GF X5 KSIZEV(OUT-OF Al *SIZE)}
35 NIL

36 (TALK)

37 ((LOEATE TRIANGLE MLL UDEATE BLUE X2)CIDEATE MEDIUM X3))

38 ((OUT-GF Xl &SHAPE) { GUT-OF A2 =CGLOR}{OUT-OF AB *SIZE))

39 (LISP SETQ XL NTL} -
400 {LISP SETQ Xe NIL)

Gi CC TOEATE RIGHT-OF XLICLOEATE ABUVE K29)
A3 , ( (QUT-CF RIGHT~Ge KRAVE OUT-OF ABQVE *RA))
44 {(OUT-OF LEET-OF RRBICCUT-OF BELCH *RBDY
4&5 ({IDEATE LEFT-OF XLPMTDEATE BELLY 4223
&4 NIL

31
  
     
  
   
 
 

   

     
   
   
  
 
 

StOG Tea oe)
use : astrugtion, but instead us
of productis fhe fi pussibility is thot the emits
non in- language 4 ing.
evi ang not understand pas ives
DES yarsible. It see

or petween subject,
ecarnaul ars when asked to
Similarly ut Zz

Ter

hat we

pe ref ct
u oO
rf

 
    
   

precedes

Se

sho g

probadi sentence fes
priate taneously producing tne
were : > 1 the measures of produce
Ferm: Le 79) ing d 72 ring procedures ,

es 42

we Ve Cia ee ee ee

 

   

with a HAM network of propositions sagged 25 to-pe-spoken and
a topic o sentence. The topic of the sentence will correspond to the
first neaning-oearing : etwor! cpraxX searches through 1vs
START network Looking £0 a7 4 xen proposition
attached to the topic and woich expresse pic 28 first element. It
determines wnetner @ path accomplishes this by evaluating the actions associated
witn @ p2acn ang determining if they created a structure that appropriately
matches the +o-be-spoxen structure. When it finds such @ path it uses iv for
generation. *

Generation is accomplished by evaluating the conditions along the path.
cursively

v a
If a condition involves 4 push to én embedded networs SPeAK is re
c speak some gub-purase expres

allied to sing @ proposition attached to one
proposition. The arguments forarecursive call of FUSH ere the expedded net-
work and the node that connects the main proposition and the emoedded ororo-
sition. if the condition does not involve 4 TUSH it will contain a set of
menory commands specifying that some features ‘Qe true OF @ word. % will use
these features to determine what the word is. Tae ~ord so Ceterminec will

3

to
nh
  

As an example, consider how SPHAK nerate a sentence correspoading
to the HAM structure in Figure 6 using , the En .eLish-11is ce amar in
“Boeure 6. Figure 6 contains set of propositions about thre denoted
by the nodes G2k6, G195, and G182. Of node C26 it is ass “ht is 2
srianghe, and shat G195 is right of it. Of G195 it is ass it is a
square and that it is above G1g2. OF e182 it is asserted © SQUAT,
smalL, and red. igure 7 1 jliustrates the generavion of this froa
GRAMMAR2. LAS enters tne START network invent cn producin ueterance
exoout G95. Thus, the topic is G1i95 (it could neve been G26 OX 162). he
first path through the network involve vedi n aaa OE G195, pet
the ve clas Tre second pata

here is nothing in the adjectiv

through tac SPART network corr ESsDo ones “say eyout G95 -~- -
it is above G182. Tuer afore, LAS pia Ss main proposition.
First, it must find some noun phrase “to express G195- The substructure under
G195 in Figure 8 reflects the construction of this. $s supnebvork « The NP network
jis called which prints the and calls NPL wpicn retrieves Square and calls
CLAUSE which prints that, . is, ana right-of and ain recursively calls NP

to print the squert- Sinilarly, recursive calls ere made on the HPL network
to express G162 as the small red square.

 

The actual. sentence generated is senna on choice of topic
START network. Given the seme to-be-s =.xf network, but the topic G2k6,
alt

 

 

    

 

SPEAR generated A tri jangle is Left—o8 : mn gauere %o2% is above a sheik red square.
Given the topic GiLg2 it generated Ax Foauase thas is below 4 SGUarS that is
right-of a triangle gle is smelt. Note ho the cnoice of tne reletion words lefc-
of vse 2 SYehecot and or Guove VS- below is Gependent on choice of topic.

It is interesting to inquire what is the Linguistic power of LAS as &
speaker. Clearly it can generate eny conbext-Tree Tanguegse since its transition
networks correspond, in structure, to a con ree grammar However, it turns
out that LAS nas certain context-sensitive asnects because its productions are
constrained by the requirement that they express Some well-formed HAM conceptual
structure. Consider two proolems that Sky (1957) regarded as not handled
well by context-free pramnars: The first 15 agreenent of number between 4 sub-
ject NP and vero. This is hard to arrange in @ context-free grammer because
the NP is already puilt py the time the choice of verb number must be made
The solution is trivial in LAS——wnen 09 4 the NP and yerb are spoken their. num-
per is determined by ins spection of whatever concept in tne ~o-be-spoken structure
underlies the subject. The other- Chomsky © xanple involves the identity of
solutionel restrictions for active and 2 passive senvences.- Thais is also achieved
au tomatically in LAS, since the restr ieticns in both cases are regarded simply
as reflections of restrictions in the sera ntic structure from which both sen-
tences are spoken.
While LAS can hendle those features of naturel language suggestive of

contexb-sensitive rules, it cannot handle ex amples Like languages of the form

oms
i

oO Ss

aNpich which require context-sensiti jive grammars. it is interesting, however ,
that it is nerd to find natural languese sentences of this structure. Tne best
T can cone up with ere respectively-tyPe sentences, G-+B-2 John and Bill nit and
kissed Jene and Mary . respectively: This sentence is of questioaasle aoceptavil
fe
ns

ATE / |
GIG

RICHT-OF SQuaA

oO
Se

The towbe-SspoK

oT

Mae

sy CHAM network for the

  
    

SPKBAK programe
Pp
wo
THE NPL
TRIAN
A tree structure sh
These networks were called
G195 which exoressed the in

3D:

NPL
RED G182
i
NPL
SQUARE

owing the network eallisand ward evtout

wwe

rating a sentence about

sion contained in Figure 6,
 

 

ture

   

 

is 3 Vv; °

sider the possibility thet the failure may a Zo sip a
on thet peti TT fer % possible to have to back into 4 network a second

ontr o £

De.
time to 2ote .
the UNDERSTAUD prog
trol structure Were

 

  
  
  
    

 

 

 

 

 

 

 

    
  
 
 

 

 

Perhavs an Englisn example would be useful +o motivate the ne oO:

control Structure. DB he two sentences tre Deroeratic party hones to win
in '76 with The Democrs carty hopes ere hign for ‘To. A main parsing network
would call a noun. vars ork to identify the Sirgt noun phrase. Suppose
UNDERSTAND identi? * ratie party. bLeve elements in the second sentence
would indicate narerore, the mein netvork would have
to re-enter ths :fferent parsing to retrieve
The Deamocresic 4ereq the noun-pnrase network
to retrieve on i% must remember woien persings 44 tried tne first time
so that it doe ievye the same old parsing. Tae complexities of this
control ssruct wiped in a more compleve report (Anderson, 1975).

Here © “lib gu a general strucwre of the gr

to find some pata START network waich wibl 3 e

parsing of the sentence. + evaluates tae eeceprsollity o

eveluating the conditions associated with that path. ond on ma

thet certain features Sa true of words in the sentence. This is Setermined by
checking memory. Alternatively, 4 condition caa require & pusn to an embedded
network. This network must parse some subphrase of tne sentence. When LAS finds
an acceptable path Shrougn a network it wilh collect tne actions along that path

to create a temporary mory structure to rep

 

 

 

 

>

exemple of wnere it might seem that LAS would need &
In English noun phrases, it seems we can heave en arbitrary numbe

me

that LAS has parsed. This, for instance, given 4 antence, Tne square thet is
risht-of the triangle is abd na TAS woulé parse it in tae
form illustrated for Pigure 6

in LAS. 1, understandin

first displayed exanp

(1973) comes closest

analysis.

It is also of interest to consider the power of LAS as an acceptor of lan~
guages. it is clear that LAS as presently constituted can acceps exactly the
context-free languages. This is because, unlike Woods' (1970) syster, actions
on ares cannot influence the results of conditions o4 arcs, and therefore, play
no role in determining wnether & string is accepted or not. However, “nat 15
interesting is that LAS's behavior aS 2m Janguese understander is relatively
Little affected by its Limitations on grammatical powers Consider the following

a n

o

contextb-sensitive gramnars
x mber of adjectives.

36
 

General Conditions Tor Language Acquisition

_— Dn ne a

 

 

Having mov reviewed now LAS. 2 understands and produces sentences, I will -
present ne three asveces of the induction progres: BRACE, SDRAXYTSST, and
GeieRALIZS. Before doing SO» it is wise to priefly state tne conditions uncer
waien LAS learss 4 Language. tt is assumed that LAS. 1 already nes CONnceDpes
attached to the words of the Languege» Tiat is, jexicalizetion is complete.
Phe task of LAS. L is to learn the grammar of the janguage--that is, how to 9
from a string of words to 4% representation of their combined meaning. Secause
Li oy concerned with Learnings meanings, it cannot be a Ver! realistic
econd 1 learning where many concepts can transfer fron she
yage. i Will propose extensions oF 43, L concerned
ZS.
52° LAS. 1 is that if works in 2 particularly restricted
semantic co is presented with pictures indicating relations ana proper-
ties ail geometric objects. These pictures ave aetueliy encoded
into sonal networs representation. Along with these pietures
LAS 2 anees describing the picture and an indication of tnat
aspec which corre sponcs to the main proposition OL TRE SUULEHES:
From 0: nm input, @ network grammar 1s constructed. The semantic
dona y simple, put the goal is to be able to learn eny natural or
natoral-like Language which may. gescribe that domain.

A major aspect of the LAS project is the BRACKET progran. Tais is an alsori-
for taking # sentence of an aroitrary Jeanguage nd HAM concepsual structure anc
sroducing @ pracketing of the senvence shat i

nis surface structure prescribes the hirerarc
sentence. For BRACKET to succeed, Four condition

etworks required to parse the
must be satisried by the infor

Condition 1. All content words in the sentence correspond to element
cepsuel structure. This amounts to the clain that the teacher is 4 L

the learner to conceptualize the information in his sentence. It does not. matte
to tne BRACKET al oritnm whether there is more information in the conceptual
Pp

structure than in the sentence.

Q
°

ndition 2- The content words in tne sentence are connected to the elements
in, the conceptual structure.. Psychologically, this amounts to the c nat
exicelization is complete. That is, the Learner KNOWS ne meanings of the wor

3

e surfece structure snterconnecting the content words is isomer
phic in its connectivity to & janguage-fres prototype structure.

Condition 3. *

 

37
a eae ’ + : . . : . . - . :
Condgition 4. The main proposition am conceptual structure 2s indicated.

 

iw
0
5.
fr
a

quire considerab
S 2 prototype
tar I will explain why soueb

   

 

 

Consider Panel (2) of Figure 6 which iit sbrueture for the
series of propositions in the English sentence re is noove the small
cirele. Panel (b) illustrates a grapu deformation of that structure giving the
surface structure of the sentence, OVS how elements within the sane nom phrase
are appropriately assigned to the same subtree. Note that the prototype struc~
ture is not specific with respect to which Links sre avove whien otners and
which ere right of which oceners. Althouga the HAM structure in Panel (aj is
get forth in a particular spatial array, the choice is arbitrary. In contrast,
the surface structure of a sentence does specify the spatial relation of links.
Tt seems reasonable that all natural languages nave as their semantics the same
order-free protovuype network. They differ from one eno ther in (a) the spatial
ordering their surface structure assigns to the networs and (bo) the insertion
of non-ucaning-bearing i moras mes into the seatence., however, the surface
structure of all natural languages ig derived from the same graph patteras.
Penei (c) of Figure & shows how the prototyse structure of Panel (a) can pro- -
vide the surface structure for 4 sentence of the artificial GRAMMARL. All the
sentences of GRAMMAR] preserve the connectivity of the underlying HAM structure.

S$ OF L
By this critericz, at least, GRAMMAR could be 4 natural language.

tain conceivable languages would have surface structures which
jons oF the underlying structure. Panel (d) illustrates

COU 1 bad G
such a hyvothetical language with the same syntectic structure as English, but
with difver Le

+

the

ent rules or semantic interpretation. In tnis languege the adjective
inz the object noun modifies the subject noun. As Panel (a) illus-
trates, there 1s no deformation of the protovyD sructure in Panel (a) to
achieve a suctace structure for the sentences in the language. No matter r how

it is attempted some renches must cross.

n

{a

uy
OR
oO
cr Yl Gs

$

connectivity of the prototype network to infer what the

LAS will use the t t

connectivity of the surface structure of the sentence must be. The network

does not specify the rignt—Lett ordering of the yrancnss or the above-below or-
dering. The rignt—Left ordering can be inferred simply from the ordering or
the words in the sentence. However, to specizy the aoove-below ordering, BRACKET
needs one further piece of information. Figure 9 illustrates an alternate
urface structure that could have been assign ad to the string in Figure 8 (c).

t might be translated into English syntex as Cir ula +

ig the small thing the

 

rear

ures illustrate, the Has

 

s below the red square. Clearly, #S these two s
Reet

r

network and the sentences are not enough tc spe eify the hierarchical ordering
of subtrees in the surface structure. The difference between the sentences

in Figure 8 (c) and 9 is the choice of wnich proposition is principal and
which is subordinate. If PRACKET is also given information as to the main
proposition it can then unambigiously retrieve the sentence's surface structur

The assumption that PRACKET is given the main propo
to the claim that the teacher can direct the learne
asserted in the sentence. Thus, in Panel (ce), the te c would direct the

learner to the picture of a red triangle above @ srall circle. He would both
have to assume that the learner properly conce tyalized the picture and that
he also realized the aboveness relation was what was peing asserted in the picture.

38
 

ad nab tnnre + y*

(b)

W

  

1 SQUARE CIRCLE SMALL .

SN UTP RTNIT V*

 

 

(c):

 

  

iM

 

 

 

SMALL . ceules RED BELOW
ructures of the sentences
graph
in (a).
aA Aarnfarm f,

The surface st
in (b) and (ce) are

the HAM structure Panel (a)

to 2

a

THE SMALL wales

|

deformations ol.
é..
FIgure J. Alternate Surs

hoa

face structure for the s

entence in Figure 9c.»
More on tre Graon Deformation Condition

a

 

 

en

 
  
 
    

T think tht the graph deformation cond

aa

ae
of 8 universal property ot language. However, to make a
is elear that something ther than the HAM network wilh neve to
3 neh > works weil en

   
   
 
 

1 a nn

sroused togeuner.

  

ed

ture ceria

    

  

closer tog? nd open are closer together. If Fig
fro

sentences woiecn alternated words
is no deformation of the structure

or.
2
ae

type, LAS cold n
groups. Por ins
would provice 2

 
 

o’ cr O

or John opened with a key the do

   

sentences Wal

    
 

 

the HAM structure to cross. ‘This Saglisna sentence &
Hien violate © deformation condition Tor Figure 19
6 :

snething Lixe the case
ually necessiple from §

erguments are equally
= posed by the verp open is one posed
bat

vary and its arguneres woile it is like

Ly
3-2 some natural language. There are two Ways to deal
could resort to a memory peprepenvalivn Tithe (uy. HOR
ar or significant considerations that motivate tne HAM
(a}. Moreover, representations like (o) finesse ons
questions in Languege acquisition--nc7 we learn the
ax verbs. To address this question Wwe need a represen-

$i-argument verbs into @ representation Like

tt
bE

ike (

semantic function of the case arguxents. Learning the role
anguage then involves leerning hoy to assign it

o a structure Tike (a). Tf will sketch systen to do this

If we Keep the HAM representations then some changes are required in BRACKET

grepn deformation condition. Whet is characteristic of multi-argumens verds
in HAM is thet the arguments are interconnected py causal relations as in (a).

Thus, BRACKST showle pe made to treat all the terminal argucent

structures &5 defining @ single level of nodes in a graph structuz

nected to 4 single root node. Tnat is, BRACKET can treat a HAM structure

such as (a) if it were (b) for purposes of utilizing tne graph reformation con-
fact, BRACKET already does tnis in the current jmplenentation.

QO

dition. In G5
The Details of ARACKET's Output
So fer, only & deseription of how one would retrieve tne surface struc
ture connectng the content words of the sentence nos been given. Suppose
MACKET were given A triangle is lett-of a scuere shat is ebove & small red
this senvence wnien will

ce

square. A bracketing structure must be imposed on

41

    
JOHN PURN KEY CAUSE. DOOR OPEN

(d)

JON KEY -—s OPEN DOOR

Figure 10. Alternative prototype structures for the sentence Jorn
onened the door with a “eve The HAM structure in (a)

introduces too many distinctions:

42
 

 

also include the functicos words, Given enis sentence ane she conceptual Ssoruc~

ture in Figure 6, BRACES rev ned (G257 (G2k6 Geby 2@ triangle) is Left-of (G195

Gi96 a square (Gig5 G225 tna. is above (G1b2 Gi83 a smell (q182 G1s5 red (G182

Gi8h square))))))- Tae oain mroposition is 2257 which is give? as the first

term in the bracketing. Tre first bracketed suD-2xoression aescribes the 3uo-

ject noun o element in the sub-exXpre gon G2b6 is the node tnat
h aa * =

 

 

 

 

jinks the Te rst

two words as

The next two worGs is.

propositions corressouaing & chese The re ft

corresponds. to 3 description of the element G1u95. Tne first emoedded prop si G

Gi95 asserts this object is 4 square and tne secoad proposition, G225, asserts

that Gi95 is above Gio2. Note that the G225 proposition is emoedded a5 4 supe

expression within tne G196 proposition. Te last element in the G226 proposi-

tion is (G182 G1i83 11 (G Gi85 red (C182 Gish squere))). Tsais exoression
G

p
wi
» FA
wy
b

162
has in it three propositions G183, G

ut. of BRACKET. Aostractly, the out—

The above @X@sp 0G
a by tne following three yexrite rues:

a
put of BRACKST may be specifi

1. S* proposition element
2, elemene + word
> ejenent > (topic S)

That is, eacn OF veted output is 2 proposition node followed yy 2 sequence of
" nese elements are either rewritten @5 words (rule 2) or
ans (rule 3)- A pracketed subexpression pegins with 6

tes the connection between the emoedded and embedding
ants within an exoression @re either non-meaning pearing

=

et ct

elements (rate 1,
bracketed gubexoress
topic node which ind
propositions. The €
words or elements corresponding to sudject, predicate, relation and ooject
in the propositio ote that BRACKET induces @ correspondence between &
level of pracketing and 4 single proposition. Zach Level of pracketing will
also correspond to a new network in LAS!s grammar. Because of the modularity

aay

of HAH propositions, e modularity 15 acnieved for the grammatical networks.
When a number of embedded propositions are attached to the same node, they
are envedded within one another in 4 right-oranching manner.

e is no semantic features to indicete waere they belong.

Ws

The insertion of non-~function words into the bracketing is 4 troublesome
problem becaus® Yr
Consider the first word 2 in the exemple sentence above in Figure 6. It could
have been placed in she top level of bracketing OF in the subexpression con~
taining triangle. Currently, all the function words to the right of ¢ content
word are placed in the sane level 2s the content word. The bracketing is
closed jmnediatels after this content word. Therefore, is is not placed in
the noun-pnrase prackeving This heuristic seems to work more often than not.
However, there clearly are cases where it will not work. Consider the Sen~
eat. ‘The current BRACKET program would

a
vo
Ss

tence The boy who Jane spoke to was a

return this 2s ((fne boy who dane spose) ) to was deaf). That is, it would
not identify to 4s in the relative clause - Sinilerly, non-meaning-bearing
suffixes like gender would not be retrieved 45 part of the noun by this
heuristic. However, there is 2 strong cue to make pracketing appropriate in
these cases. There tends to be 4 pause aften morphemes Like to. Perhaps such

  

43
pause structures could be called woon to help the BEACHES? prograa decide how
to insert the non-meaning~bearing morphemes into tne bracketing.

   

aring morphemes pose further problems besides
such morphemes in a noun phrase. Thes seq
nat, in principle, might constitute &n aroit
ets semantic referent could provice no cues
t language. Therefore, we would be back to 4
ag duction tasz that ve naracterized in the i
comro gz to observe that the structure of these st g
non-meaning~bear ing morphemes tends to pe very Simple. There are nob many
exumples of tnese strings being longer than a single word, Thuc, LL Seems
baat the languages consti tuted by these non-ne aning-veuring strings are nothing
m than very simple finite cardinality lenguages which posc, in themselves,
no serious induction problems. The yarious stretches of non-mzaning- peacing
morpnemes in a senvence could also have complex intercer endencss thereby posing
t hese

serious induction problems. Again it does not seen
= pet
simple gust at those points where it would hav

on program to Work.

a

o be the cas¢ that thes

ndencies exist. So once again we find that the structure of natural language
a to be for a LAS-like induc-

0

(

ch pte
ce

In concluding this section I should point out one example sentence which
BRACKET cannot currently hendle. They are respec tively sentences Like Jonn and
Bill Ganced end laughed resvectively. ‘The problem wW Will such a sentence is th at

1 ~ is the following prototype structure:

1 2

ba}
rd

Jonn dance Bill | lauga

Thus , John and dance are close together and so ere Bill and laugh. However,
tne sentence intersperses these elements just in the wa! way that nak makes bracketing
impossible. There are probably other exe moles like this , but IT cannot think
of them. Fortunately, this is not an utterance that appears early in child
speech nor is a particulerly simple one for adults, Of all the grammatical
constructions, the respectively construction is the one that most suggests tne
need to have trensformational rules in the gramcar.

 

s capable of

Te funetion of SPEAKTEST is to test wnether its i
ely modify the grammar 50

generating a sentence and, if it is not, eappropr lat
that it can. SPSAKTEST is called after BASCART 15 complete. It receives’.
from BRACKET a HAM conceptual structure, @ pack ted sentence, the main pro-
position and the topic of the sentence. As in the SPEAX program SPEANTSOT
attempts to find some path through its network which will express a proposi-
sion attached to the topic. iz it succeeds no modifications are made to the
network. If it cannot, & new path is built through the network to incorporate
the sentence.
ts
5
ae
3)
id
“
©
a]

The best way to understand the operavion af &
through one example. ‘ine target Language js wag given to le
a, GR

’ arn is illustrated
in ail Lh. ais is a very simple languase, yasieally GRAMMAR of Table 1. it
nas a smaller vocabulary +9 make it more tractable, The reason for choosing
this Lan: guage is that it is of just surricient complexity to jllustrate LAS's
acquisition mechenisns. In addition, LAS hes learned GHAMMAR2, also given in

Table l.

 

 

Figure iL 4llustrates LAS's
come in. Tre first sentence i
returned by BRACKET es (GiT4 (GLL5 6 &
CLT refers to the main proposition given as an ar t
this is LAS'ts first sentence of the languag® the sr network will, of course,
completely fail to parse the sé

ntence. It has no Ff mnar yet. Therefore,
it induces the top-level START network in Fi 1. “A listing of the czact
s given below th

are information induced is e graphical illustrati on in Figure ll.
Since the first two elements acer GLT4 i in the bracketed senvence are them-
sees bracketed, the Tir network will ne pushes to subo-

rc

st two arcs in the
e contains 4 conaition om the word aoove. The restric-
is that it be & enber of tre word class Aig? . This class Was
+ this senvence and only contains the. word above at this point.

: d a path through ene START network, SPEAKTEST checks the
a % che

 

Having now conssrucee

subnetworks in thas path to see whether they can hendie the bracketed subexpres

sions in the sentence. Tis is accomp ished by 2 recursive cail to SPRANTES?T.

For tne first phrase SPRAKTEST 15 called, taking aS ar suzents the network AL95,
a (GLLo sq 4 aes =

Pe netrrertk A105 the word class

 

ana “Uue . UUpLe oa
nm square, and in network ALOT the word class A22] con~
2 se two Suonetworks should pe the same in ea final grammar

4 prepared to risk such @ genera alizavion at this point.

 

Note in this example how the ore ee provided by BRACKET completely
specified the em bedding of network The sentence provided by BRACKET was
(Girl (G11L5 G116 square) (G148 culo. triangle) esove). The first element GLT
ag the main proposition. The second element (G11L5 G116 square) was 4 bracketed
wubexpression indicating 2 subnetwork shoule be ereated. Similarly, the third
expression indicated a sion network. Tae last element above Was @ single word

and so could be hendled by | memory condition in the mein network.

 

 

 

The second sentence is triangle sauare rignt-of. This is transformed by
BRACKET.to (G315 (G2k6 G2k7 triangle) (6283 G264 square) right-of). Because
s sentence cannot be handled by tre

 

 

or the narrow one-member word c classes this Ss

current grammar. However, SPSAKTEST does not add new network arcs to nandle
the sentence. Rather, it expands wor class AL9G to include right-of, word
class A211 to include ‘riensle, and word class A22h +9 include square. The
grammar is now at such @ stage that LAS couid speak cr understand the sentences
triangle sauare above or squ uare sauare ricnt-of and other sentence s which it
had not studied. Thus, elready the Firss generalizations have een made. LAS
can produce and unders tend novel sentences.

This illustrates the type of generalizations that are: mage Weta une
SPRAXTEST prograa. For instance, consider @ ge
SPEAKTEST decided to use the existit i
—_
fa
eee”
op
j-
re
Or
WY
om
re?
“AI
Wy
ay
kr
In
5

( (SQUARE) (PRIANCLE) ABOVE)

 

oa ALS . - £19? . 1199
START-—— 95 yy gh 9 te STOP

r
A197 _ 2 AZZ) STOP

 

 

 

Pp
. S Ne
C247 C316 Case
7 NX | V N 3/ P

; iA?
PRIANGLE Gate RIGH?-OFr ass SQUARE -

( (@RIANGLE) (SQUARE) RIGHT =OF)
A199——— ABOVE, RIGHT -OF

A211 ty SQUARE, TRIANGLE

 

A22h-——— TRIAN GLE, SQUARE

Figure 1}. LAS"s treatment of the first two sentences in the
induction sequence»

46
Anderson

the first wot ra of th

work Al9D that head been cr

to include triangle Both deci
al

e second sentence . his involved (a) using tne same subpnet—
xc (ob) expanding the word class A2i1

ons “Tested on semantic criteria. The networn

ge attached to the main propo-

5f the node G2h6 which is
this identity of semantic

 

   

In making these general izations, SPEAXTAST is making a strong assumptlon
about the nature of huey ‘Language. This assumption is stated as Condition

Condition 5. Words or phrases with identical semen tie functions at identical
a tat chically. This is the assumption

points in a network behave identically syn u

of semantic-induced equivalence of syntex. it is another way in wnica senantic
information fac jlitates grammar induction. it clearly need not be true of an
arbitrary lang uage. For insvance, Gecisions made in tne sudject noun phrase
might in theory condition syntactic decisicn made in the object noun phrases.

4
LAS. because of its heuristics in SPEAKEES?T for generalization, would not be

> D se
able to learn such a language.

4

wee ee BRK TAG

Figure 12 illustrates LAS's networ % gremiar after two more sentences have
g

- ‘ = 7
come in. penvences 3 ana 4 LinVOLVe tise

 

aubuiua Ota Giaw arenes pans 7
treats these 4s syntactic variants of abo ve en rigat— of which differ in their
assignment of their noun phrase ArguURENnts © 9 the “Logical categories subject and
object. Therefore, LAS creates an alternat 2 branch through its START network

to accommodate this possibility.

Figure 13 illustrates the course of LAS's learning. Altogether LAS will
is will have to meke three extra

a

be presented 14h sentences. Subsequently,
generaliza ations to capuure the entire vaerget lenguage. Piotted on the ebscissa
s this learning history and along the ordinave we have the natural Logarithm
of the number of sentences which the gremmar can handie. This is a finite

4 e, unlike GR MMAR2, and therefore tne number of sentences in the language
will always be finite. As can be seen fron Figure 13, by the fourth sentences
LAS's gramaar is adequate to handle 16 sentences.

 

LAS's grammar arter the next five senvences ig illustrated in Figure Lh,
These are LAS's first encounters with two word noun phrases. ALL five sentences
involve the relations right-of and above and therefore result in the elaboration

of the A195 and Al9T suo-netWOrks. Consifer the first sentence, square red
ariangle blue above, which is retrieved oy PRACKET as (C329 (C270 Ce71l square
(C270 C272 red)) (6303 C304 triangle (C2 ove) Ce sider

0}. Con
C

+

ct

03 C305 blue) above
the parsing of the first noun phrase. Hote that the adjec 1
is embedded within the larger noun vohrase. This is an example of the right
embedding woich BRACKET always imposes on @ sentence. ‘This will cause SPEAK
TEST to create a push to an anbadded network within its A195 subnetwork. As
can be seen in Figure Ls the exiscing arc containing the A2LL word class
is kept to handle square. Two alternetive arcs are added—-one with a push to

 

47
 

Birnrea 12

LEU Uf.
> - aA

LAS*s eranmar aqdVver

-_

mom MDYANAT TS
SQuUARL mda Gis

1 s Whee Se
Zo TRIANGLE SQUARE
2, SQUARE TRIANGLE

TRIANGLE SQUARE

 

   
 
  

RQ Se STOP
54
~ « £4221 _—
Alge = above,r:
art 4 Poe rn ac >
meow ™~ Rt whi FONT wet
of

Hl

it

i

H

scuare, vriangle
calow, left-of

square, triangle
square, triangle

18
nonyta e
¥. GQAKMAR

ANDER

Load

1?
i

Apo
NwoeOO  4

ie
~

ym NETSEE

ob

=
Ce bs

eyo
4 iv 0

7
by io fui

atti
‘eet

Ma

2500 1

 

 

274
fo

¢
”

1040 “

-

. ©
Ou 1296

True sentences +
over generalizations
N a
: ie OB
ee a
008 True sentences
a
LOO j—
20\— e/
/
—_—
ef
/ bol whe \
1

 

Figure 13+

5 & 5 6 7 8 9 20 Tt “V2 130 ~«4

Sentences Studied

The growth of LAS's grammar with its learning history:

ve deses be ce nee

1 2° ° 37
Ceneralizations
Additions to LAS’s grammar after studying:

Le SQUARS RED PRIANGLS BLUE ABOVE

2, PARTANGLE LARGE SQUARE SMALL RIGHT-OF
3° TRIANGLE RED PRIANCLE RED ABOVE

i. SQUARE SMALL MRTANCLE RED RIiGHP-OF
5, SQUARE BLUE TRIANGLS LARGE RIGHT-OF

A211 HB

\ NIL
SS TOE

€ A221 C560
$9 STOP

NIL

r

——-== STOP
. o £0585
ough colo Ss. stop C560 STOR

C510 = gmail, blue,large,red
small, blue, large red

C3

oat

co

Or
!

50
A nde SCO 50rh

   
 
  

the Chgl nebvos : ce with a NIL trens: io sithin the Cho: network

the word cle “yord red.

Supeose &

neato! ontein saquence : DOs 2 phrase
“ Psat -
AS fully parsed.
i? fares A ao
- ee a 1
= . 3 Ly a?
ok, assigned to
1 . Huils 4
tne Cx OULLG. Kw
A
_ ar C : --. An,
oy a . ; 1,3 _%
Als 2S rth are Laat : € LS on mazing to

7% ge
des-induczed equivalence of

(a) illustrete how
son in natural language.
at, eta. He would set
a 7
wed by any noun. Suppose, he
LiS as The + poy + 's.7
oe network illustra tea in
sion that foots is the
: Y 7S
Jian is, af eaurse,
ge lege vvin, 1964). What
n2

a te carious oe paegoneralize
% OS + there ere 2 nunib er of alterna—

uch norphenic rules is.
=

tives end no semantic vasis zo choose besween them. Because of its principle
of sementics-induced equivalence of syntax, LAS will eo yonerelize in those
situation Apparently, ¢ children ere opet rating under 4 similar rule.

LiS needs to be endowed with a mechanism to allow it to recover from such
overgeneralizations . Therefore, One of tne future additions to LAS will have
to be a RECOVER Prose Consider how it would work witn this pluralization

example. Suppos LEARNMORE receives the genience The Scat ave above the
triancie. In oe cating to analyze the sentence in SPPAXTSST, the plural
foots will be generated put Will mismet ch the sentence. RECOVER has as its

function to note such mismatches. since ait is possible that there are two
alternate Ways of expressing plurality, RECOVER cannot assucs its grammar is
wrong. Rather it will interrupt the information flow and check the accepta-
bility of The foots are above OF the triangle. Tat is, RECOVER will explicitly
seek neg ative information. Upon 7 Learnan ne exoression is ungrammatical

gv
RECOVER will teke foot out of the word cless that is pluralized py 's.

 

1 . .
fo accomplish this T would have to put within TAS some ‘pechanism that will
segnent words into their morpnenes.

50