UNKNOUN UOROI MPLA :SYNTACTIC EXPECTATION!. NOUN SERRNTIC EXPECTATION; FRANC: ATRONS PTRONS SLOTI RECIP REQ, ILOC ROTOR COflPLETEO.. FACTION OF mARGOt.fiG |NFEflNCE, eflUSSIAe RTRRNS
Trang 1T O W A R D S A S E L F - E X T E N D I N G PARSER
Jaime G Carbonell Department Of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213
A b s t r a c t
This p a p e r discusses an approach to incremental
learning in natural language processing The
t e c h n i q u e of projecting and integrating semantic
c o n s t r a i n t s to learn word definitions is analyzed
as Implemented in the POLITICS system
E x t e n s i o n s and improvements of this technique
a r e d e v e l o p e d The problem of generalizing
e x i s t i n g word meanings and understanding
m e t a p h o r i c a l uses of words Is addressed In terms
o f s e m a n t i c constraint Integration
1 I n t r o d u c t i o n
N a t u r a l language analysis, like most other subfields of
A r t i f i c i a l I n t e l l i g e n c e and Computational Linguistics, suffers
from t h e f a c t t h a t computer systems are unable to
a u t o m a t i c a l l y b e t t e r themselves Automated learning ia
c o n s i d e r e d a v e r y difficult problem, especially when applied
t o n a t u r a l language understanding Consequently, little e f f o r t
ha8 b e e n f o c u s e d on this problem Some pioneering work in
A r t i f i c i a l i n t e l l i g e n c e , such as AM [ I ] and Winston's learning
s y s t e m 1"2] s t r o v e to learn or discover concept descriptions
in w e l l - d e f i n e d domains Although their efforts produced
i n t e r e s t i n g Ideas and techniques, these techniques do not
f u l l y e x t e n d to • domain as complex as natural language
a n a l y s i s
R a t h e r t h a n a t t e m p t i n g the formidable task of creating a
l a n g u a g e learning system, I will discuss techniques for
I n c r e m e n t a l l y Increasing the abilities of a flexible language
a n a l y z e r There are many tasks that can be considered
" I n c r e m e n t a l language learning" Initially the learning domain
Is r e s t r i c t e d to learning the meaning of new words and
g e n e r a l i z i n g e x i s t i n g word definitions There ere a number of
A.I t e c h n i q u e s , and combinations of these techniques
c a p a b l e of e x h i b i t i n g incremental learning behavior I first
d i s c u s s FOULUP and POLITICS, two programs that exhibit a
limited c a p a b i l i t y for Incremental word learning Secondly, the
t e c h n i q u e of semantic constraint projection end Integration,
a s Implemented in POLITICS, Is analyzed in some detail
Finally, I discuss the application of some general learning
t e c h n i q u e s to t h e problem of generalizing word definitions
e n d u n d e r s t a n d i n g metaphors
2 L e a r n i n g F r o m S c r i p t E x p e c t a t i o n s
L e a r n i n g w o r d definitions In semantically-rich c o n t e x t s Is
p e r h a p s o n e of t h e simpler tasks of incremental learning
Initially I confine my discussion to situations where the
m e a n i n g o f a word can be learned from the Immediately
s u r r o u n d i n g c o n t e x t Later I relax this criterion to see how
g l o b a l c o n t e x t and multiple examples can help to learn the
m e a n i n g of unknown words
T h e FOULUP program [ 3 ] learned the meaning of some
u n k n o w n w o r d s in the c o n t e x t of applying s script to
u n d e r s t a n d a s t o r y Scripts [4, 5] are frame-like knowledge
r e p r e s e n t a t i o n s a b s t r a c t i n g the important features and
c a u s a l s t r u c t u r e of mundane events Scripts have general
e x p e c t a t i o n s of the actions and o b j e c t s that will be
e n c o u n t e r e d in processing a story For Instance, the
r e s t a u r a n t s c r i p t e x p e c t s to see menus, waitresses, and
c u s t o m e r s ordering and eating food (at d i f f e r e n t
p r e - s p e c i f l e d times In the story)
FOULUP took a d v a n t a g e of these script e x p e c t a t i o n s to
c o n c l u d e t h a t Items r e f e r e n c e d in the story, which w e r e part
o f e x p e c t e d actions, w e r e Indeed names of o b j e c t s that the
s c r i p t e x p e c t e d to see These e x p e c t a t i o n s w e r e used to form d e f i n i t i o n s of n e w words For instance, FOULUP induced
t h e meaning of " R a b b i t " in, "A Rabbit v e e r e d off the road
a n d s t r u c k a t r e e , " to be a self-propelled vehicle The
s y s t e m u s e d information about the automobile accident script
t o m a t c h t h e unknown word with the script-role "VEHICLE",
b e c a u s e t h e s c r i p t knows that the only o b j e c t s that v e e r o f f
r o a d s t o smash Into r o a d - s i d e obstructions ere self propelled
v e h i c l e s
3 C o n s t r a i n t P r o j e c t i o n In P O L I T I C S
The POLITICS system E6, 7] induces the meanings of
u n k n o w n w o r d s b y a one*pass syntactic and semantic
c o n s t r a i n t p r o j e c t i o n followed by conceptual enrichment from
p l a n n i n g and w o r l d - k n o w l e d g e inferences Consider how POLITICS p r o c e e d s when It encounters the unknown word
" M P L A " In analyzing the sentence:
" R u s s i a s e n t massive arms shipments to the MPLA In Angola."
S i n c e "MPLA" follows the article '*the N it must be a noun,
a d j e c t i v e or a d v e r b After the word "MPLA", the preposition
" i n " Is e n c o u n t e r e d , thus terminating the current
p r e p o s i t i o n a l phrase begun with "to" Hence, since all
w e l l - f o r m e d prepositional phrases require a head noun, and
t h e " t o " p h r a s e has no other noun, "MPLA" must be the head noun Thus, b y projecting the syntactic constraints
n e c e s s a r y for t h e s e n t e n c e to be well formed, one learn8
t h e s y n t a c t i c c a t e g o r y of an unknown word it Is not always
possible t o narrow the categorization of a word to a single
s y n t a c t i c c a t e g o r y from one example In such cases, I
p r o p o s e I n t e r s e c t i n g the s e t s of possible s y n t a c t i c
c a t e g o r i e s from more then one sample use of the unknown
w o r d until t h e Intersection has a single element
POLITICS learns the meaning of the unknown word by a similar, b u t s u b s t a n t i a l l y more complex, application of the
s a m e p r i n c i p l e of p r o j e c t i n g constraints from other parts of
t h e s e n t e n c e and s u b s e q u e n t l y Integrating these constraints
t o o o n e t r u o t a meaning representation In the example
Trang 2a b o v e , POLITICS analyzes the verb "to send" as either i n
ATRANS or s PTRAflS (Schank [ 8 ] discusses the Conceptual
D e p e n d e n c y case frames Briefly, a PTRANS IS s physical
t r a n s f e r o f location, and an ATRANS Is an abstract transfer
POLITICS c a n n o t d e c i d e on the t y p e of TRANSfer is that it
d o e s n o t k n o w w h e t h e r the destination of the transfer (i.e.,
t h e MPLA) Is s location or an agent Physical objects, such
a s w e a p o n s , are PTRANSed to locations but ATRANSed to
a g e n t s The c o n c e p t u a l analysis of the sentence, with MPLA
as y e t u n r e s o l v e d , Is diagrammed below:
*SUSSIA* <-~
• [ C I P S l < i s > LOC v i i ~qNGOLAe
t
l
mlq.R) RTRRNS • d IN, iq[CIPill
I I N < ,,ffi/$SIRi,
I
J~ERPONe <ls~ NWISER vii (, llOMI)
W h a t has t h e a n a l y z e r learned about "MPLA" as s result of
f o r m u l a t i n g t h e CD case frame? Clearly the MPLA can only be
an a c t o r (I.e., s person, an Institution or s political e n t i t y in
t h e POLITICS domain) or s location Anything else would
v i o l a t e t h e constraints for the recipient case In both ATRANS
e n d PTRANS Furthermore, the analyzer knows t h a t the
l o c a t i o n o f t h e MPLA Is Inside Angola This Item of Information
is i n t e g r a t e d w i t h the case constraints to form a partial
d e f i n i t i o n o f "MPLA" Unfortunately both Iocatlcms and actors
c a n b e l o c a t e d inside countries; thus, the identity of the
MPLA is still not uniquely resolved POLITICS assigns t h e
n a m e RECIP01 t o the partial definition of "MPLA" and
p r o c e e d s t o a p p l y Its Inference rules tO understand the
p o l i t i c a l Implications of the e v e n t Here I discuss only the
I n f e r e n c e s r e l e v a n t for further specifying the meaning of
-MPLA m
4 U n c e r t a i n I n f e r e n c e in L e a r n i n g
POLITICS Is a goal-driven tnferencer It must explain ell
a c t i o n s In terms of the goals of the actors and recipients
The emphasis on inducing the goals of actors and relating
t h e i r a c t i o n s t o means of achieving these goals is Integral to
t h e t h e o r y o f s u b j e c t i v e understanding embodied in
POLITICS ( S e e [ 7 ] for a detailed discussion.) Thus, POLITICS
t r i e s t o d e t e r m i n e how the action of sending weapons can be
r e l a t e d t o t h e goals of the Soviet Union or any other possible
a c t o r s i n v o l v e d in the situation POLITICS k ~ s that Angola
w a s Jn a s t a t e of civto war; that Is, a s t a t e w h e r e political
f a c t i o n s w e r e .'xerclstng their goals of taking military and,
t h e r e f o r e , political control of a country Since po6ssssing
w e a p o n s Is a precondition to military actions, POLITICS infers
t h a t t h e r e c i p i e n t of the weapons may have been one of the
poliUcal factions (Weapons ere s means to fulfUllng the goal
o f • p o l i t i c a l faction, t h e r e f o r e POLITICS Is able to explain
w h y t h e f a c t i o n w a n t s to r e c e i v e weapons.) Thus, MPLA Is
I n f e r r e d to be a political faction This Inference is Integrated
w i t h t h e e x i s t i n g partial definition and found to be
c o n s i s t e n t Finally, the original action Is refined to be an
ATRANS, as t r a n s f e r of possession of the weapons (not
m e r e l y t h e i r k:mation) helps the political faction to a c h i e v e
Its m i l i t a r y goal
N e x t , POLITICS tries to determine how sending weapons to s
m i l i t a r y f a c t i o n can further the goals of the Soviet Union Communist countries have the goal of spreading their '
I d e o l o g y POLITICS concludes that this goal can be fulfilled
o n l y if t h e g o v e r n m e n t of Angola becomes communist Military aid t o s political faction has the standard goal of military
t a k e o v e r o f the government Putting these t w o f a c t s
t o g e t h e r , POLITICS concludes that the Russian goal can be
f u l f i l l e d if t h e MPLA, which may become the n e w Angeles
g o v e r n m e n t , is Communist The definition formed for MPLA Is
a e f o l l o w s :
QI~'I i~a1"~ tntrvI (OPS flPLA (POS NOUN (TYPE PROgI[R)))
(TOK efllq.A.) )
(PARTOF luRN6OLR.) (|oEOLOGY ~¢OiltlUN|STe) (GORLSt ((ACTOR (*flPLA*) iS
(SCONT O§JI[CT (dN6OLRe)
Vm (IR)))))P
The r e a s o n w h y memory entries are distinct from dictionary
d e f i n i t i o n s is t h a t t h e r e is no o n e - t o - o n e mapping b e t w e e n
t h e t w o For Instance, "Russia" and "Soviet Union" are t w o
s e p a r a t e d i c t i o n a r y entries that refer to the same concept in
m e m o r y Similarly, the c o n c e p t of SCONT (social or political
c o n t r o l ) a b s t r a c t s Information useful for the goal-driven
i n f e r e n c e s , but has no corresponding e n t r y in the lexicon, as
I f o u n d no e x a m p l e w h e r e such concept was e x p l i c i t l y
m e n t i o n e d In n e w s p a p e r headlines of political conflicts (i.e., POLITICS' domain)
Some o f t h e I n f e r e n c e s t h a t POLITICS made a r e much more
p r o n e t o e r r o r than others More specifically, the s y n t a c t i c
c o n s t r a i n t p r o j e c t i o n s and the CD case-frame projections
e r e q u i t e certain, but t h e goal-driven Inferences are only
r e a s o n a b l e g u e s s e s For Instance, the MPLA coWd h a v e b e e n
• p l a t e a u w h e r e Russia dePosited Its weapons for l a t e r
d e l i v e r y
5 A S t r a t e g y f o r D e a l i n g w i t h U n c e r t a i n t y
G i v e n such possibilities for error, t w o possible s t r a t e g i e s to
d e e i w i t h t h e problem of uncertain inference come to mind
F i r s t , t h e s y s t e m could be r e s t r i c t e d to making only the more
c e r t a i n c o n s t r a i n t p r o j e c t i o n and integration inferences This
d o e s n o t usually produce s complete definition, but t h e
p r o c e s s may b e I t e r a t e d for o t h e r exemplars w h e r e t h e
u n k n o w n w o r d Is used in d i f f e r e n t semantic c o n t e x t s Each
t i m e t h e n e w word Is encountered, the semantic constraints
a r e i n t e g r a t e d with the previous partial definition until a
c o m p l e t e definition is formulated The problem with this
p r o c e s s Is t h a t it may require a substantial number of
i t e r a t i o n s t o c o n v e r g e upon s meaning representation, end
w h e n it e v e n t u a l l y does, this representation wtll not be as
rich a s t h e r e p r e s e n t a t i o n resulting from the less certain
g o a l - d r i v e n i n f e r e n c e s For Instance, it would be impossible
t o c o n c l u d e t h a t t h e MPLA was Communist and w a n t e d to
t a k e o v e r Angola only b y projecting semantic constraints
The s e c o n d method is based on the system's ability to
r e c o v e r from i n a c c u r a t e inferences This is the method i
i m p l e m e n t e d in POLITICS The first step requires the
d e t e o t l o n o f contradictions b e t w e e n the Inferred Information
e n d n e w Incoming information The n e x t step is to assign
Trang 3blame t o t h e a p p r o p r i a t e culprit, i.e., the inference rule t h a t
a s s e r t e d t h e i n c o r r e c t conclusion Subsequently, the system
must d e l e t e t h e inaccurate assertion and later inferences
t h a t d e p e n d e d upon it (See [ 9 ] for a model of truth
m a i n t e n a n c e ) The final s t e p is to use the new information to
c o r r e c t t h e memory entry The optimal system within my
p a r a d i g m would use a combination of both strategies - It
w o u l d u s e Its maximal Inference capability, r e c o v e r when
I n c o n s i s t e n c i e s arise, and i t e r a t e over many exemplars to
r e f i n e and confirm the meaning of the new word The first
t w o c r i t e r i a are p r e s e n t in the POLITICS implementation, but
t h e s y s t e m sto~s building a new definition after processing a
s i n g l e e x e m p l a r unless it d e t e c t s a contradiction
L e t us b r i e f l y t r a c e through an example where PC~.ITICS la
t o l d t h a t the MPLA is indeed a pisteau after it inferred the
meaning t o b e a political faction
I POLITICS Pun - - 2/06/76 !
• : INTERPRET US-CONSERVRT IVE)
INPUT STORY, Russia sent massive arms ship.eats
to the flPL.A in Re,gels
PARSING (UNKNOUN UOROI MPLA)
:SYNTACTIC EXPECTATION! NOUN)
(SERRNTIC EXPECTATION; (FRANC: (ATRONS PTRONS) SLOTI RECIP
REQ, ILOC ROTOR))) COflPLETEO
CREATING N( u MEMORY ENTRY, *flPLRo
INFERENCE, ~,MPLRo MIAY BE A POLXTICI:n FACTION OF mARGOt.fiG
|NFEfl(NCE, eflUSSIAe RTRRNS eRRMSo TO tAPLRo
INFERENCE; *MPLAe IS PNOOROLY aCOflMUNXSTe
INFERENCE, GOAL OF aMPLRa IS TO TAK( OVEN eANOOl.Ae
INSTANTIATING SCAIPTJ SRIONF
I Question-salem- dialog )
441hst does the MPLA ~ent the arms foP?
TNE RPLR MANTa TO TAKE OVER RNGOLR USING THE NEIMONS
I~he( might the ether factionS in An(iolll de?
THE OTHER FACTIONS NAY ASK SORE OTHER COUNTRY FOR RRflS
| Reading furthcP Input ]
INPUT STORY; + T h e Zunqabl faction oleoPatlng fPoe the I~PLA
plateau received the $ovist uealNme
PARS |NO CONPLETEO •
GREAT|NO NEW N(NORY ENTRY: aZUNGRO|a
ACTIVE CONTEXT RPPLJCRItLE, ~IONF
C1 ISR CONFLICT, eMPLRe ISR (eFRCTIONo sPI.RTERUe)
(ACTIVATE' (|NFCN(CK C|)) R(OUEST(O
C2 SCRIPT ROLE CONFLICT,
(&R[O-RECXP |N SRIOMF) • aMPLRe RNO aZUNGABIe
(ACTIVATE (INFCHECK C2)) RE~JEST[O
(INFCHECK C1 C2) INVOKEOt
RTTERPT TO MERGE MEMORY ENTRIES, (*M~.Ae aZON~Ia) FAIUJRE'
INFER(lICE RULE CHECK(O (RULEJFI SRIOMF) OK
INFERENCE RUt.E CHECKED (flULEIGO) CONFLICT!
OELETING RESULT OF RULE/GO
C2 RESOt.VEDt ~f'~'LRe ]SA *PLRTEIqJe IN eRNGOLRs
C2 flESOLVEO; UlAI?-RECIP IN SRIOMF) • eZONGROIo
REDEFINING enPLRe AS eZUNGRe|O COMPI.IrTEO
CREATING HEM orlPLRo fl(NORY (NTNY CORPLET(O
POLITICS realizes t h a t there is an Inconsistency In Its
I n t e r p r e t a t i o n w h e n It tries to integrate "the MPLA plateau"
w i t h its p r e v i o u s definition of "MPLA" Political factions and
p l a t e a u s ere d i f f e r e n t conceptual classes Furthermore, the
n e w Input s t a t e s that the Zungsbl received the weapons,
n o t t h e MPLA Assuming that the Input Its correct, POLITICS
s e a r c h e s for an Inference rule to assign blame for the
p r e s e n t contradiction This Is done simply by temporarily
d e l e t i n g t h e result of each inference rule that was a c t i v a t e d
in t h e original i n t e r p r e t a t i o n until the contradiction no longer
e x i s t s The rule t h a t concluded that the MPLA was a political
f a c t i o n Is found to resolve both contradictions If deleted
S i n c e r e c i p i e n t s of military aid must be political entitles, the MPLA b e i n g s geographical location no longer qualifies as a
m i l i t a r y aid recipient
Finally, POLITICS must check whether the inference rules
t h a t d e p e n d e d upon the result of the deleted rule are no
l o n g e r a p p l i c a b l e Rules, such as the one that concluded t h a t
t h e p o l i t i c a l f a c t i o n was communist, depended upon there
b e i n g a political faction receiving military aid from Russia The Zungabi now fulfll:s this role; therefore, the inferences
a b o u t t h e MPLA are t r a n s f e r e d to the Zungabl, and th~ MPLA
Is r e d e f i n e d to be a plateau (Note: the word "Zungabl" was
c o n s t r u c t e d for this example The MPLA is the present ruling
b o d y o f Angola.)
6 E x t e n d i n g t h e P r o j e c t a n d I n t e g r a t e M e t h o d
T h e POL)TICS Implementation of the p r o j e c t - a n d - i n t e g r a t e
t e c h n i q u e ts b y no means complete POLITICS can only
I n d u c e t h e meaning of c o n c r e t e or proper nouns when there
Is s u f f i c i e n t c o n t e x t u a l information In a single exemplar Furthermore, POLITICS assumes that each unknown word will
h a v e o n l y one meaning In general It is useful to realize when
a w o r d Is u s e d to mean something other than Its definition,
a n d s u b s e q u e n t l y formulate an alternative definition
I I l l u s t r a t e t h e c a s e where many examples are required to
n a r r o w d o w n t h e meaning of s word with the following
e x a m p l e : "Johnny told Mary that If she didn't give him the
t o y , he would <unknown-word) her." One can induce that the
u n k n o w n w o r d Is a verb, but its meaning can only be guessed
at, In g e n e r a l terms, to be something unfavorable to Mary For I n s t a n c e , t h e unknown word could mean " t a k e the o b j e c t
f r o m " , or " c a u s e injury to" One needs more then one
e x a m p l e of t h e unknown word used to mean the same thing
In d i f f e r e n t c o n t e x t s Then one has s much richer, combined
c o n t e x t from which the meaning can be p r o j e c t e d with
g r e a t e r precision
Figure 1 diagrams the general p r o j e c t - a n d - i n t e g r a t e algorithm This e x t e n d e d version of POLITICS' word-learning
t e c h n i q u e a d d r e s s e s the problems of iterating o v e r many
e x a m p l e s , multiple word definitions, and does not restrict its Input t o c e r t a i n c l a s s e s of nouns
7 G e n e r a l i z i n g W o r d D e f i n i t i o n s
W o r d s c a n h a v e many senses, some more n"neral than
o t h e r s L e t us look at the problem of gen lizlng the
s e m a n t i c definition of a word Consider the case where
" b a r r i e r " is d e f i n e d to be a physical o b j e c t that dlsenables a
t r a n s f e r of location (e.g "The barrier on the road Is blocking
my w a y " ) Now, l e t us interpret the sentence, "Import quotas form a b a r r i e r to International trade." Clearly, an Import quota
Is n o t • p h y s i c a l o b j e c t Thus, one can minimally generalize
" b a r r i e r " to mean "anything that d i s c s h i e s s physical
t r a n s f e r o f location."
L e t us s u b s t i t u t e " t a r i f f " for "quota" In our example This
s u g g e s t s t h a t our meaning for "barrier" is insufficiently
g e n e r a l A t a r i f f cannot disensble physical transfer; tariffs
d i m e a b l e willingness to buy or sell goods Thus, one can
f u r t h e r g e n e r a l i z e the meaning of barrier to be: "anything
t h a t d l a e n a b l e e any t y p e of transfer", Yet, U r e a trace of t h e
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g e n e r a l i z a t i o n process must be remembered because t h e
original meaning is o f t e n preferred, or metaphorically
r e f e r e n c e d Consider: "The trade barriers w e r e lifted • and
"The n e w legislation bulldozed existing trade barriers •
r h e a s s e n t e n c e s can only be understood metaphorically
r h a t is, one needs to r e f e r to the original meaning of
~barrier" as a physical object, In order for •lifting" or
' b u l l d o z i n g " t o make sense After understanding the literal
leaning o f a "bulldozed barrier", the n e x t step Is to infer
he c o n s e q u e n c e of such aft action, namely, the barrier no
) n g e r e x i s t s Finally, one can refer to the generalized
l e a n i n g o f " b a r r i e r " to i n t e r p r e t the proPoaltion that •The
e w l e g i s l a t i o n caused the trade barriers to be no longer In
x i e t e n c e "
p r o p o s e t h e *ollowing rules to generalize word definitions
l d u n d e r s t a n d metaphorical references to their ortglnol,
mmel definition:
1 ) If t h e definition of a word violates the semantic
c o n s t r a i n t s p r o j e c t e d from an interpretation of the
r e s t o f t h e sentence, c r e a t e a new word-sense
d e f i n i t i o n t h a t copies the old deflnltiml minimally
r e l a x i n g (I.e., generalizing) the violated constraint
2 ) In Interpreting new sentences always prefer
t h e mast specific definition if applicable
3 ) If t h e generalized definition Is encountered
again i n Interpreting t e x t , make It part of the
p e r m a n e n t dictionary
4 ) If • word definition requires further
g e n e r a l i z a t i o n , choose the existing most general
d e f i n i t i o n and minimally r e l a x Its violated semantic
c o n s t r a i n t s until a new, y e t more general definition
Is formed
5 ) If t h e c a s e frame formulated in interpreting a
s e n t e n c e p r o j e c t s more specific semantic
c o n s t r a i n t s onto the word meaning than those
c o n s i s t e n t with rite entire sentence, Interpret the
w o r d usln(! t h e most specific definition c o n s l s t e t
w i t h t h e c a s e frame If the resultant meaning of
t h e c a s e frame Is inconsistent with the
i n t e r p r e t a t i o n of the whole sentence, Infer the most l i k e l y consequence of the pMtlally-build
C o n c e p t u a l D e p e n d e n c y case frame, and use this
c o n s e q u e n c e In Interpreting the rest of the
s e n t e n c e
The p r o c e s s d e s c r i b e d b y rule 5 enables one to Interpret the
m e t a p h o r i c a l uses of words like " l i f t e d " and "bulldozed" In
o u r e a r l i e r e x a m p l e s The literal meaning of each word i8
a p p l i e d t o t h e o b j e c t case, (i.e., "barrier•), and t h e Inferred
c o n s e q u e n c e (i.e., destruction of the barrier) i8 used t o
I n t e r p r e t t h e full s e n t e n c e
8 C o r a l c l i n g R e m a r k s
T h e r e a r e a multitude of w a y s to incrementally Improve t h e
l a n g u a g e understanding capabilities of a system In this
p a p e r I d i s c u s s e d in some detail the process of learning n e w
w ~ r d e In l e s s e r d e t a i l I presented some ideas on how t o
g e n e r a l i z e word meanings and Interpret metaphorical uses of
i n d i v i d u a l w o r d s There are many more aspects to learning
l a n g u a g e and understanding metaphors that I have not
t o u c h e d upon, For Instance, many metaphors transcend
I n d i v i d u a l w o r d s and phrases Their Interpretation may
r e q u i r e d e t a i l e d cultural knowledge [ 1 0 ]
In o r d e r t o p l a c e some p e r s p e c t i v e on p r o j e c t - a n d - i n t e g r a t e
l e a r n i n g method, consider t h r o e general learning mechanisms
c a p a b l e o f implementing d i f f e r e n t aspects of Incremental
l a n g u a g e learning
L e a r n i n g h y e x a m p l e This Is perhaps the most
g e n e r a l learning s t r a t e g y From several exemplars,
o n e can i n t e r s e c t the common concept by, If
n e c e s s a r y , minimally generalizing the meaning of
t h e known part of each example until a common
a u b p a r t Is found by Intersection This common
e u b p a r t Is likely to be the meaning of the unknown
s e c t i o n o f each exemplar
L e a r n i n g b y n e a r - m i s s analysis Winston [ 2 ]
t a k e s full a d v a n t a g e of this technique, i t may be
u s e f u l l y applied to a natural language system that
c a n I n t e r a c t l v e i y g e n e r a t e utterances using the
w o r d s it learned, and l a t e r be told whether It used
t h o s e words c o r r e c t l y , whether It erred seriously,
or w h e t h e r It came close but failed to understand
a s u b t l e n u a n c e In meaning
L e a r n i n g b y c o n t e x t u a l e x p e c t a t i o n EasanUally FOULUP and POLITICS use the method of
p r o j e c t i n g c o n t e x t u a l e x p e c t a t i o n s to the
Trang 5linguistic element whose meaning Is to be Induced Much more mileage can be gotten from this method, especially If one uses strong syntactic
c o n s t r a i n t s and expectations from other
k n o w l e d g e sources, such as s discourse model, s
n a r r a t i v e model, knowledge about who is providing
t h e information, and why the information Is being
p r o v i d e d
9 R e f e r e n c e s
T
2
3
4
5
6
7
8
9
TO
Lenet, 0 AMz Discovery In Mathematics as
Heuristic Search Ph.D Th., Stanford University,
1977
Winston, P Learning Structural Descriptions from
Examples Ph.D Th., MIT, 1970
Granger, R FOUL-UPt A Program that Figures Out Meanings of Worcls from Context IJCAI-77, 1977 Schank, R C and Abelson, R.P Scripts, Goals, Plans and Unclerstancling Hillside, NJ: Lawrence Erlbaum, 1977
Cullingford, R Script Appllcationt Computer Uncleratandlng of Newspaper Stories Ph.D Th., Yale University, 1977
Carbonell, J.G POLITICS: Automated Ideological Reasoning Cognitive Science 2, 1 (1978), 2 7 - 5 1 Carbonell, J.G Subjective Unclerstancllng:
Computer Mo<lels of Belief Systems Ph.D Th., Yale University, 1979
Sohsnk, R.C Conceptual Information Processing
Amsterdam: North-Holland, 1975
Doyle, J Truth Malntenanoe Systems for Problem Solving Master Th., M.I.T., 1978
Lakoff, G and Johnson, M Towards an
Experimentalist Philosopher: The Case From Literal
M e t a p h o r In preparation for publication, 1979