Towards a Self-Extending Lexicon* 
Uri Zernik 
Michael G. Dyer 
Artificial Intelligence Laboratory 
Computer Science Department 
3531 Boelt~r Hall 
University of tMifomis 
Los Angeles, California 90024 
Abstract 
The problem of manually modifying the lexicon 
appears with any natural language processing program. 
Ideally, a program should be able to acquire new lexieal 
entries from context, the way people learn. We address 
the problem of acquiring entire phrases, specifically 
Jigurative phr~es, through augmenting a phr~al lezico~ 
Facilitating such a self-extending lexicon involves (a) 
disambiguation~se|ection of the intended phrase from a 
set of matching phrases, (b) robust 
parsin~-comprehension of partially-matching phrases, 
and (c) error analysis---use of errors in forming hy- 
potheses about new phrases. We have designed and im- 
plemented a program called RINA which uses demons to 
implement funetional-~rammar principles. RINA receives 
new figurative phrases in context and through the appli- 
cation of a sequence of failure-driven rules, creates and 
refines both the patterns and the concepts which hold 
syntactic and semantic information about phrases. 
David vs. Goliath 
Native: 
Learner: 
Native: 
Learner: 
Native: 
Learner: 
Native: 
Remember the s~ory of David and Goliath? 
David took on Goliath. 
David took GoltLth sons,here? 
No. David took on Goliath. 
He took on him. He yon the fight? 
No. He took him on. 
David attacked him. 
He ~ok him on. 
He accepted She challenge? 
Right. 
Native: 
Learner: 
Here in annt,her story. 
John took on the third exam question. 
He took on a hard problem. 
Another dialogue involves put one's foot do~-a. Again, 
the phrase is unknown while its constituents are known: 
Going Punk 
1. Introduction 
A language understanding program should be able 
to acquire new lexical items from context, forming for 
novel phrases their linguistic patterns and figuring out 
their conceptual meanings. The lexicon of a learning 
program should satisfy three requirements: Each lexical 
entry should (1) be learnable, (2) facilitate conceptual 
analysis, and (3) facilitate generation. In this paper we 
focus on the first two aspects. 
1.1 The Task Domain 
Two examples, which will be used throughout this 
paper, are given below. In the first dialogue the learner 
is introduced to an unknown phrase: take on. The 
words take and on are familiar to the learner, who also 
remembers the biblical story of David and Goliath. The 
program, modeling a language learner, interacts with a 
native speaker, as follows: 
* This work w~s made possible in part by s grant from the Keck 
Foundation. 
Native: 
Learner: 
Native: 
Learner: 
Jenny vant,ed ~o go punk, 
but, her father put, his toot dovu. 
He moved his foot dora? 
It, doen not, mike sense. 
No. He put his foot, dora. 
He put his foot dovu. 
He refused to let her go punk. 
A figurative phrase such as put one's fooc down is a 
linguistic pattern whose associated meaning cannot be 
produced from the composition of its constituents. 
Indeed, an interpretation of the phrase based on the 
meanings of its constituents often exists, but it carries a 
different meaning. The fact that this literal interpreta- 
tion of the figurative phrase exists is a misleading clue in 
learning. Furthermore, the learner may not even notice 
that a novel phrase has been introduced since she is fam- 
iliar with dram as well as with foot. Becker \[Becker?5\] 
has described a space of phrases ranging in generality 
from fixed proverbs such as charity begsns at, home 
through idioms such as Xay dove t,he tar and phrasal 
verbs such as put, up rich one's spouse and look up the 
name, to literal verb phrases such as sit, on she chair. 
He suggested employing a phrasal lexicon to capture this 
entire range o( language structures. 
284 
1.2 Issues in Phrase AequLsition 
Three issues must be addressed when learning 
phrases in context. 
(I) Detecting failures: What are the indications that 
the initial interpretation of the phrase take him on 
as "to take a person to a location" is incorrect? Since 
all the words in the sentence are known, the problem 
is detected both as a conceptual discrepancy (why 
would he take his enemy anywhere?) and as a syn- 
tactic failure (the expected location of the assunied 
physical transfer is missing). 
(2) Determining scope and generality of patterns: 
The linguistic pattern of a phrase may be perceived 
by the learner at various levels of generalit~l. For ex- 
ample, in the second dialogue, incorrect generaliza- 
tions could yield patterns accepting sentences such 
as: 
Her boss put his left foot down. 
He moved his foot dora. 
He put down his foot. 
He put dovn his leg. 
(3) 
A decision is also required about the scope of the 
pattern (i.e., the tokens included in the pattern). 
For instance, the scope of the pattern in John put up 
with Mary could be (I) ?x:persoa put:verb up where 
with is associated with l'lmry or (2) ?x:persos 
put:verb up with ?y:persou, where with is associated 
with put up. 
Finding appropriate meanings: The conceptual 
meaning of the phrase must be extracted from the 
context which contains many concepts, both ap- 
propriate and inappropriate for hypothesis forma- 
tion. Thus there must be strategies for focusing on 
appropriate elements in the context. 
1.3 The Program 
RINA \[Dyer85\] is a computer program designed to 
learn English phrases. It takes as input English sentences 
which may include unknown phrases and conveys as out- 
put its hypotheses about novel phrases. The pro~am 
consists of four components: 
(l) Phrasal lexicon: This is a list of phrases where 
each phrase is a declarative pattern-concept pair 
\[WilenskySl\]. 
(2) Case-frame parser: In the parsing process, case- 
frame expectations are handled by spawning demons 
\[Dyer83\]. The parser detects comprehension failures 
which are used in learning. 
(3) Pattern Constructor: Learning of phrase patterns 
is accomplished by analyzing parsing failures. Each 
failure situation is associated with a pattern- 
modification action. 
(4) Concept Constructor: Learning of phrase concepts 
is accomplished by a set of strategies which are 
selected according to the context. 
Schematically, the program receives a sequence of 
sentence/contezt pairs from which it refines its current 
pattern/concept pair. The pattern is derived from the 
sentence and the concept is derived from the coLtext. 
However, the two processes are not independent since 
the context influences construction of patterns while 
linguistic clues in the sentence influence formation of 
concepts. 
2. Phrasal Representation of the Lexicon 
Parsing in RINA is central since learning is 
evaluated in terms of parsing ability before and after 
phrases are acquired. Moreover, learning is accomplished 
through parsing. 
2.1 The Background 
RINA combines elements of the following two ap- 
proaches to language processing: 
Phra~-bued pattern matching: In the imple- 
mentation of UC \[Wilensky84\], an intelligent help system 
for UNIX users, both PHRAN \[AJ'ens82 l, the conceptual 
analyzer, and PHRED \[Jacobs85\] the generator, share a 
phrasal lepton. As outlined by Wilensky {Wilensky81\] 
this lexicon provides a declarative database, being modu- 
larly separated from the control part of the system which 
carries out parsing and generation. This development in 
representation of linguistic knowledge is paralleled by 
theories of functional grammars {Kay79\[, and lezical- 
functional grammars \[Bresnan78\]. 
Ca~,-b,,-,,ed demon pmming: Boris \[DyerS3 I 
modeled reading and understanding stories in depth. Its 
conceptual analyzer employed demon-based templates 
for parsing and for generation. Demons are used in pars- 
ing for two purposes: (1) to implement syntactic and se- 
mantic expectations \[Riesbeck74\] and (2) to implement 
memory operations such as search, match and update. 
This approach implements Schank's \[Schank77\] theory of 
representation of concepts, and follows case-grammar 
\[Fillmore681 principles. 
RINA uses a declarative phrasal lexicon as sug- 
gested by Wilensky \[Wilensky82\], where a lexical phrase 
is a pattern-concept pair. The pattern notation is 
described below and the concept notation is Dyer's 
\[Dyer83\] i-link notation. 
285 
2.2 The Pattern Notation 
To span English sentences, RINA uses two kinds 
of patterns: lezical patterns and ordering patterns 
\[Arens82\]. In Figure I we show sample lexical patterns 
(patterns of lexical phrases). Such patterns are viewed as 
the generic linguistic forms of their corresponding 
phrases. 
I. ?x: (animate.a~ent) nibble :verb <on ?y: food> 
2. ?z: Cpernou.Lgent) tLke:verb on ?y:p,tlent 
3. ?x: (person.a~ent) <put:verb foot:body-part do~m> 
Figure h The Pattern Notation 
The notation is explained below: 
(t) A token is a literal unless otherwise specified. For ex- 
ample, on is a literal in the patterns above. 
(2) ?x:sort denotes a variable called .~x of a semantic 
type sort. ?y:food above is a variable which stands 
for references to objects of the semantic class food. 
(3) Act.verb denotes any form of the verb s!lntactic 
class with the root act. nibble:vet6 above stands for 
expressions such as: nibbled, hms never nibbled, 
etc. 
(4) By default, a pattern sequence does not specify the 
order of its tokens. 
(5) Tokens delimited by < and > are restricted to 
their specified order. In Pattern I above, on must 
directly precede ?y:food. 
Ordering patterns pertain to language word-order con- 
ventions in general. Some sample ordering patterns are: 
active: <?x:agenr. ?y: (verb.~tive)> 
passive: <?x:pattent ?y: (verb.p~,.s£ve)> 
*<by ?Z : agent> 
infinitive:<to ?x: verb. active> "?y: Iq~ent 
Figure 2: Ordering Patterns 
The additional notation introduced here is: 
(6) An * preceding a term, such as *<by ?z:~ent> in 
the first pattern above indicates that the term is op- 
tional. 
(7) * denotes an omitted term. The concept for Ty in the 
third example above is extracted from the agent of 
the pattern including the current pattern. 
(8) By convention, the agent is the case-frame which 
precedes the verb in the lexical pattern. Notice that 
the notion of agent is necessary since (a) the agent is 
not necessarily the subject (i.e., she vu taken) and 
{b) the agent is not necessarily the actor {i.e., she 
received the book, he took a blo~), and (c) in the 
infinitive form, the agent must be referred to since 
the agent is omitted from the pattern in the lexicon. 
(9) Uni/ieation \[Kay79\] accounts for the interaction of 
lexical patterns with ordering patterns in matching 
input sentences. 
So far, we have given a declarative definition of our 
grammar, a definition which is neutral with respect to ei- 
ther parsing or generation. The parsing procedure which 
is derived from the definitions above still has to be given. 
2.3 Parsing Objectives 
Three main tasks in phrasal parsing may be 
identified, ordered by degree of difficulty. 
(1) Phrase dlaambiguation: When more than one lexi- 
cat phrase matches the input sentence, the parser 
must select the phrase intended by the speaker. For 
example, the input the vorkeru took to the streets 
could mean either "they demonstrated" or "they were 
fond of the streets'. In this case, the first phrase is 
selected according to the principle of pattern 
speci\]icit 9 \[Arens821. The pattern ?X: person 
taXe:verb <to the streets> is more specific then 
?x:person take:verb <to ?y:thing> However, in 
terms of our pattern notation, how do we define pat- 
tern specificity? 
{2) Ill-formed input comprehension: Even when an 
input sentence is not well phrased according to text- 
book grammar, it may be comprehensible by people 
and so must be comprehensible to the parser. For 
example, John took Nary school is telegraphic, but 
comprehensible, while John took Nzry to conveys 
only a partial concept. Partially matching sentences 
(or "near misses') are not handled well by syntax- 
driven pattern matehers. A deviation in a function 
word (such as the word to above) might inhibit the 
detection of the phrase which could be detected by a 
semantics-driven parser. 
(3) Error-detection: when the hypothesized phrase 
does not match the input sentence/context pair, the 
parser is required to detect the failure and return 
with an indication of its nature. Error analysis re- 
quires that pattern tokens be assigned a case- 
significance, as shown in Section 4. 
Compounding requirements--disambiguation plus 
error-analysis capability-- complicate the design of the 
parser. On one hand, analysis of "near misses" (they 
bury a hatchet instead of they buried the hatchet) can 
288 
be performed through a rigorous analysis--assuming the 
presence of a single phrase only. On the other hand, in 
the presence of multiple candidate phrases, disambigua- 
finn could be made efficient by organizing sequences of 
pattern tokens into a discrimination net. However, at- 
tempting to perform both disambiguation and "near 
miss" recognition and analysis simultaneously presents a 
difficult problem. The discrimination net organization 
would not enable comparing the input sentence, the 
"near miss", with existing phrases. 
The solution is to organize the discrimination se- 
quence by order of generality from the general to the 
specific. According to this principle, verb phrases are 
matched by conceptual features first and by syntactic 
features only later on. For example, consider three ini- 
tial erroneous hypotheses: (a) bury a hatchet (b) bury 
the gun, and (c) bury the hash. On hearing the words 
"bury the hatchet', the first hypothesis would be the 
easiest to analyze (it differs only by a function word 
while the second differs by a content-holding word) and 
the third one would be the hardest (as opposed to the 
second, huh does not have a common concept with 
hlttchet). 
2.4 Case-Frames 
Since these requirements are not facilitated by the 
representation of patterns as given above, we slightly 
modify our view of patterns. An entire pattern is con- 
structed from a set of case-/tames where each case-frame 
is constructed of single tokens: words and concepts. 
Each frame has several slots containing information 
about the case and pertaining to: (a) its syntactic ap- 
pearance (b) its semantic concept and (c) its phrase role: 
agent, patient. Variable identifiers (e.g., ?x. ?y) are 
used for unification of phrase patterns with their 
corresponding phrase concepts. Two example patterns 
are given below: 
The first example pattern denotes a simple literal 
verb phrase: 
{id:?x class:person role:agent} 
(take:verb) 
(id:?y class:person role:patient} 
{id:?z class:location marker:to} 
Figure 3: Cue Frmmes for "He took her to school" 
Both the agent and the patient are of the class person; 
the indirect object is a location marked by the preposi- 
tion co. The second phrase is figurative: 
{id:?x class:person role:agent) 
{take:verb} (marker:to determiner:the word:streets} 
Figure 4: Case Frames for "He took to the streets" 
The third case frame in Figure 4 above, the indirect ob- 
ject, does not have any corresponding concept. Rather it 
is represented as a sequence of words. However the 
words in the sequence are designated as the marker, the 
determiner and the word itself. 
Using this view of patterns enables the recognition 
of "near misses" and facilitate error-analysis in parsing. 
3. Demons Make Patterns Operational 
So far, we have described only the linguistic nota- 
tion and indicated that unification \[Kay79\] accounts for 
production of sentences from patterns. However, it is not 
obvious how to make pattern unification operational in 
parsing. One approach \[Arens82\] is to generate word se- 
quences and to compare generated sequences with the in- 
put sentence. Another approach IPereiraS01 is to imple- 
ment unification using PROLOG. Since our task is to 
provide lenient parsing, namely also ill-formed sentences 
must be handled by the parser, these two approaches are 
not suitable. In our approach, parsing is carried out by 
converting patterns into demons. 
Conceptual analysis is the process which involves 
reading input words left to right, matching them with 
existing linguistic patterns and instantiating or modify- 
ing in memory the associated conceptual meanings. For 
example, assume that these are the phrases for take: in 
the lexicon: 
?x:person take:verb ?y:person ?z:locale 
John took her to Boston. 
?x:person take:verb ?y:phys-obj 
He took the book. 
?x:person take:verb off ?y:attire 
He took off his coaL. 
?x:person take:verb on ?y:person 
David took on Goliath. 
?x:person take:verb a bow 
The actor took a boy. 
?x:thing take:verb a blow 
The vail took a blov. 
?x:person take:verb ~to the streets~ 
The vorkern ~ok t,o the streets. 
The juvenile took t,o the e~reeCs. 
Figure 5: A Variety of Phrases for TAKE 
where variables ?x, :y and ?z also appear in correspond- 
in& concepts (not shown here). How are these patterns 
287 
actually applied in conceptual analysis? 
3.1 Interaction of Lexlcal and Ordering Patterns 
Token order in the lexical patterns themselves 
(Figure 5) supports the derivation of simple active-voice 
sentences only. Sentences such as: 
Msry vas ~,zken on by John. 
A veak contender David might, have left, alone, 
bu~ Goliath he book on. 
David dec£ded to take on Gol'tath. 
Figure 6: A Variety of Word Orders 
cannot be derived directly by the given hxical patterns. 
These sentences deviate from the order given by the 
corresponding lexical patterns and require interaction 
with language conventions such as passive voice and 
infinitive. Ordering patterns are used to span a wider 
range of sentences in the language. Ordering patterns 
such as the one's given in Figure 2 depict the word order 
involving verb phrases. In each pattern the case-frame 
preceding the verb is specified. (In active voice, the agent 
appears imediately before the verb, while in the passive 
it is the patient that precedes the verb.) 
3.2 How Does It All Work? 
Ordering patterns are compiled into demons. For 
example, DAGENT, the demon anticipating the agent 
of the phrase is generated by the patterns in Figure 2. rt 
has three clauses: 
If the verb is in active form 
then the agent is immediately be/ore the verb 
If the verb is in passive form 
then the agent may appear, preceded by by. 
If the verb is in infinitive 
then the agent is omitted. 
Its concept is obtained from the function verb. 
Figure T: The Conatruction of D_AGENT 
In parsing, this demon is spawned when a verb is en- 
countered. For example, consider the process in parsing 
the sentence 
Da.v~.d dec'ideal ~ bake on ~,o\].£ath. 
Through identifying the verbs and their forms, the pro- 
tess is: 
decided (active, simple) 
Search for the agent before the verb, anticipate an 
infinitive form. 
talc, (active, infinitive) 
Do not anticipate the agent. The actor of the "take 
on" concept which is the agent, is extracted from the 
agent of "decide'. 
4. Failure-Driven Pattern Construction 
Learning of phrases in RINA is an iterative pro- 
tess. The input is a sequence of sentence-context pairs, 
through which the program refines its current hypothesis 
about the new phrase. The hypothesis pertains to both 
the pattern and the concept of the phrase. 
4.2 The Learning Cycle 
The basic cycle in the process is: 
(a) A sentence is parsed on the background of a concep- 
tual context. 
(b) Using the current hypothesis, either the sentence is 
comprehended smoothly, or a failure is detected. 
(c) If a failure is detected then the current hypothesis is 
updated. 
The crucial point in this scheme is to obtain from the 
parser an intelligible analysis of failures. As an example, 
consider this part of the first dialog:. 
1 Program: tie took on him. He von ~he fight? 
2 User:. No. He took him on. Dav'\[d Lt, ta, cked him. 
3 Program: He took him on. 
He accepted the challenge? 
The first hypothesis is shown in Figure 8. 
pattern: 
concept: 
?x:person take:verb don ?y:person~ 
?x win the conflict with ?y 
Figure 8: First Hypothesis 
Notice that the preposition on is attached to the object 
?y, thus assuming that the phrase is similar to He looked 
at Iqaar7 which cannot produce the following sentence: H. 
look.d her at. This hypothesis underlies Sentence 1 
which is erroneous in both its form and its meaning. 
Two observations should be made by comparing this pat- 
tern to Sentence 2: 
The object is not preceded by the preposition on. 
The preposition on does not precede any object. 
These comments direct the construction of the new hy- 
pothesis: 
288 
pattern: 
concept: 
?x:person take:verb on ?y:person 
?x win the conflict with ?y 
Figure 9: Second Hypothesis 
where the preposition on is taken as a modifier of the 
verb itself, thus correctly generating Sentence 3. In Fig- 
ure 9 the conceptual hypothesis is still incorrect and 
must itself be modified. 
4.3 Learning Strategies 
A subset of RINA's learning strategies, the ones 
used for the David and OoliaCh Dialog (Section 1.1) are 
described in this section. In our exposition of failures 
and actions we will illustrate the situations involved in 
the dialogues above, where each situation is specified by 
the following five ingredients: 
(1) the input sentence (Sentence), 
(2) the context (not shown explicitly here), 
(3} the active pattern: either the pattern under con- 
struction, or the best matching pattern if this is the 
first sentence in the dialogue (Patternl). 
(4) the failures detected in the current situation 
(Failures), 
(5) the pattern resulting from the application of the ac- 
tion to the current pattern (Pattern2). 
Creating a New Phrase 
A case.role mismatch occurs when the input sen- 
tence can only be partially matched by the active pat- 
tern. A 9oal mismatch occurs when the concept instan- 
tinted by the selected pattern does not match the goal si- 
tuation in the context. 
Sentence: 
Patternt: 
Failures: 
Pattern2: 
David took on Goliath. 
?x:person take:verb ?y:person ?z:location 
Pattern and goal mismatch 
?x:person take:verb 
David's physically transferring Goliath to a loca- 
tion fails since {1) a location is not found and (2) the ac- 
tion does not match David's goals. If these two failures 
are encountered, then a new phrase is created. In ab- 
sence of a better alternative, RINA initially generates 
David Cook him somevhere. 
Discriminating a Pattern by Freezing a Prepoab 
tional Phrase 
A prepoMtional mismatch occurs when a preposi- 
tion P matches in neither the active pattern nor in one 
of the lexical prepositional phrases, such as: 
<on ?x:platform> (indicating a spatial relation) 
<on ?x:time-unit> (indicating a time of action) 
<on ?x:location> (indicating a place) 
Sentence: 
Patternl: 
Failures: 
Pattern2: 
David took on Goliath. 
?x:person take:verb 
Prepositional mismatch 
?x:person take:verb <on ?y:person> 
The preposition on is not part of the active pat- 
tern. Neither does it match any of the prepositional 
phrases which currently exist for on. Therefore, since it 
cannot be interpreted in any other way, the ordering of 
the sub-expression <on ?y,:peraoa> is frozen in the larger 
pattern, using < and >. 
Two-word verbs present a di~culty to language 
learners \[Ulm75\] who tend to ignore the separated verb- 
particle form, generating: take on him instead of cake 
him o,s. In the situation above, the learner produced this 
typical error. 
Relaxing an Undergeneralized Pattern 
Two failures involving on: (1) case-role mismatch (on 
?y:p,r6oa is not found)and (2) prepositional mismatch 
(on appears unmatched at the end of the sentence) are 
encountered in the situation below: 
Sentence: 
Patte~at: 
Failures: 
Pattern2: 
David took him on. 
?x:person take:verb <on ?y'person 
Prepositional and case-role mismatch. 
?x:person take:verb on ?y:person 
The combination of these two failures indicate 
that the pattern is too restrictive. Therefore, the < and 
> freezing delimiters are removed, and the pattern may 
now account for two-word verbs. In this case on can be 
separated from ¢,&ke. 
Generaiising a Semantic Restriction 
A semantic mismatch is marked when the seman- 
tic class of a variable in the pattern does not subsume 
the class of the corresponding concept in the sentence. 
Sentence: 
Patternt: 
Failures: 
Pattern2: 
John took on the third question. 
?x:person take:verb on ?y:person 
Semantic mismatch 
?x:person take:verb on ?y:task 
As a result, the type of ?y in the pattern is generalized to 
include both cases. 
289 
Freezing a Reference Which Relates to a Metaphor 
An unrelated reference is marked when a reference 
in the sentence does not relate to the context, but rather 
it relates to a metaphor (see elaboration in \[Zernik85\] ). 
The reference his fooc cannot be resolved in the con- 
text, rather it is resolved by a metaphoric gesture. 
Sentence: 
Pattern1: 
Failures: 
Pattern2: 
Her father put his foot down. 
?x:person put:verb down ?y:phys-obj 
Goal mismatch and unrelated reference 
?x:person put:verb down foot:body-part 
Since, (I) putting his foot on the floor does not 
match any of the goals of Jenny's father and (2) the 
reference his foot is related to the domain of metaphor- 
ic gestures rather than to the context. Therefore, foot 
becomes frozen in the pattern. This method is similar to 
a method suggested by Fuss and Wilks \[Fuss83\]. In their 
method, a metaphor is analyzed when an apparently ill- 
formed input is detected, e.g.: the car drank ffi lot of 
gas. 
4.4 Concept Constructor 
Each pattern has an associated concept which is 
specified using Dyer's \[Dyer83\] i-link notation. The con- 
cept of a new phrase is extracted from the context, 
which may contain more than one element. For example, 
in the first dialogue above, the given context contains 
some salient sto W points \[Wilensky82\] which are indexed 
in episodic memory as two violated expectations: 
• David won the fight in spite of Goliath's physical su- 
periority. 
• David accepted the challenge in spite of the risk in- 
volved. 
The program extracts meanings from the given set of 
points. Concept hypothesis construction is further dis- 
cussed in \[Zernik85\]. 
5. Previous Work in Language Learning 
In RINA, the stimulus for learning is comprehen- 
sion failure. In previous models language learning was 
,~lso driven by detection of failures. 
PST \[Reeker76\] learned grammar by acting upon 
dilfercnces detected between the input sentence and 
internally generated sentences. Six types of differences 
were classified, and the detection of a difference which 
belonged to a class caused the associated alteration of 
the grammar. 
FOUL-UP \[Granger771 learned meanings of single 
words when an unknown word was encountered. The 
meaning was extracted from the script \[Schank77\] which 
was given as the context. A typical learning situation 
was The cffir vas driving on Hvy 66, vhen it careened 
off the road. The meaning of the unknown verb 
care.ned was guessed from the SACCIDENT script. 
POLITICS \[CarbonellTO\], which modeled 
comprehension of text involving political concepts, ini- 
tiated learning when semantic constraints were violated. 
Constraints were generalized by analyzing underlying 
metaphors. 
AMBER \[Langley82\] modeled learning of basic 
sentence structure. The process of learning was directed 
by mismatches between input sentences and sentences 
generated by the program. Learning involved recovery 
from both errors of omission (omitting a function word 
such as the or is in daddy bouncing ball) and errors of 
commission (producing daddy is liking dinner). 
Thus, some programs acquired linguistic patterns 
and some programs acquired meanings from context, but 
none of the above programs acquired new phrases. Ac- 
quisition of phrases involves two parallel processes: the 
formation of the pattern from the given set of example 
sentences, and the construction of the meaning from the 
context. These two processes are not independent since 
the construction of the conceptual meaning utilizes 
linguistic clues while the selection of pattern elements of 
new figurative phrases bears on concepts in the context. 
6. Current and Future Work 
Currently, RINA can learn a variety of phrasal 
verbs and idioms. For example, RINA implements the 
behavior of the learner in vffivtd vs. c, oliffich and in Go- 
£ng Punk in Section 1. Modifications of lexicM entries are 
driven by analysis of failures. This analysis is similar to 
analysis of ill-formed input, however, detection of failures 
may result in the augmentation of the lexicon. Failures 
appear as semantic discrepancies (e.g., goal-plan 
mismatch}, or syntactic discrepancies (e.g., case-role 
mismatch). Finally, references in figurative phrases are 
resolved by metaphor mapping. 
Currently our efforts are focussed on learning the 
conceptual elements of phrases. We attempt to develop 
strategies for generalizing and refining acquired concepts. 
For example, it is desirable to refine the concept for 
"take on" by this sequence of examples: 
David toak on Goliath. 
The \[t, kers took on ~he Celtics. 
I took on a, bard ~ffi,,.k. 
I took on a, hey Job. 
In selecting ~he naae °TQvard8 a. Self-EzCending 
LeXiCOne. Ye t,43olc OU in old nKme. 
29O 
The first three examples "deciding to fight someone', 
"playing against someone" and "accepting a challenge" 
could be generalized into the same concept, but the last 
two examples deviate in their meanings from that 
developed concept. The problem is to determine the 
desired level of generality. Clearly, the phrases in the 
following examples: 
~sdce on am enemy 
Lake os an old name 
~a~e on the shape of a essdce 
deserve separate entries in the phrasal lexicon. The 
question is, at what stage is the advantage of further 
generalization diminished? 
Acknowledgments 
We wish to thank Erik Muelhr and Mike Gasser 
for their incisive comments on drafts of this paper. 
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