Metaphor - A Key to Extensible Semantic Analysis 
Jaime G. Carbonell 
Carnegie-Mellon University 
Pittsburgh, PA 15213 
Abstract 
Interpreting metaphors is an integral and inescapable 
process in human understanding of natural language. This 
paper discusses a method of analyzing metaphors based on 
the existence of a small number of generalized metaphor 
mappings. Each generalized metaphor contains a 
recognition network, a basic mapping, additional transfer 
mappings, and an implicit intention component. It is argued 
that the method reduces metaphor interpretation from a 
reconstruction to a recognition task. Implications towards 
automating certain aspects of language learning are also 
discussed, t 
1. An Opening Argument 
A dream of many computational linguists is to produce a 
natural language analyzer that tries its best to process 
language that "almost but not quite" corresponds to the 
system's grammar, dictionary and semantic knowledge 
base. In addition, some of us envision a language analyzer 
that improves its performance with experience. To these 
ends, I developed the proiect and integrate algorithm, a 
method of inducing possible meanings of unknown words 
from context and storing the new information for eventual 
addition to the dictionary \[1\]. While useful, this mechanism 
addresses only one aspect of the larger problem, accruing 
certain classes of word definitions in the dictionary. In this 
paper, I focus on the problem of augmenting the power of a 
semantic knowledge base used for language analysis by 
means of metaphorical mappings. 
The pervasiveness of metaphor in every aspect of human 
communication has been convincingly demonstrated by 
Lakoff and Johnson \[4}, Ortony \[6\], Hobbs \[3\] and marly 
others. However, the creation of a process model to 
encompass metaphor comprehension has not been of 
central concern? From a computational standpoint, 
metaphor has been viewed as an obstacle, to be tolerated at 
best and ignored at worst. For instance, Wilks \[9\] gives a 
few rules on how to relax semantic constraints in order for a 
parser to process a sentence in spite of the metaphorical 
1This research was sponsored in part by the Defense Advanced 
Research Prelects Agency (DOD). Order No. 3597, monitored by the Air 
Force Avionics Laboratory under Contract F33615-78-C-155t. The 
views and conclusions contained in this document are those of the 
author, and should not be interpreted as rel3resenting the official 
policies, either expressed or implied, of the Defense Advanced Research 
Projects Agency or the U.S. Government. 
2Hobbs has made an initial stab at this problem, although h=s central 
concern appears to be ~n characterizing and recognizing metaphors in 
commonly-encountered utterances. 
usage of a particular word. I submit that it is insufficient 
merely to tolerate a metaphor. Understanding the 
metaphors used in language often proves to be a crucial 
process in establishing complete and accurate 
interpretations of linguistic utterances. 
2. Recognition vs. Reconstruction - The 
Central Issue 
There appear to be a small number of general metaphors 
(on the order of fifty) that pervade commonly spoken 
English. Many of these were identified and exemplified by 
Lakoff and Johnson \[4\]. For instance: more-is-up. 
less.is.down and the conduit metaphor - Ideas are objects, 
words are containers, communication consists of putting 
objects (ideas) into containers (words), sending the 
containers along a conduit (a communications medium. 
such as speech, telephone lines, newspapers, letters), 
whereupon the recipient at the other end of the conduit 
unpackages the objects from their containers (extracts the 
ideas from the words). Both of these metaphors apply in the 
examples discussed below. 
The computational significance of the existence of a small 
set of general metaphors underlies the reasons for my 
current investigation: The problem of understanding a large 
class of metaphors may be reduced from a reconstruction to 
a recognition task. That is, the identification of a 
metaphorical usage as an instance of one of the general 
metaphorical mappings is a much more tractable process 
than reconstructing the conceptual framework from the 
bottom up each time a new metaphor-instance is 
encountered. Each of the general metaphors contains not 
only mappings of the form: "X is used to mean Y in 
context Z", but inference rules to enrich the understanding 
process by taking advantage of the reasons why the writer 
may have chosen the particular metaphor (rather than a 
different metaphor or a literal rendition). 
3. Steps Towards Codifying Knowledge 
of Metaphors 
t propose to represent each general metaphor in the 
following manner: 
A Recoanition Network contains the information 
necessary to decide whether or not a linguistic 
utterance is an instantiation of the general 
metaphor. On the first-pass implementation I will 
use a simple discrimination network. 
The Basic MaDoinQ establishes those features 
of the literal input that are directly mapped onto 
a different meaning by the metaphor. Thus, Any 
upward movement in the more-is-up metaphor 
is mapped into an increase in some directly 
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Quantifiable feature of the part of the input that 
undergoes the upward movement. 
The Implicit.intention Comoonent encodes the 
reasons why this metaphor is typically chosen 
by a writer or sPeaker. Part of this information 
becomes an integral portion of the semantic 
representational of input utterances. For 
instance, Lakoff identifies many different 
metaphors for love: love-is-a-journey, 
love-is-war, love-is.madness, love-is-a-patient, 
love.is-a-physical-force (e.g., gravity, 
magnetism). Without belaboring the point, a 
writer chooses one these metaphors, as a 
function of the ideas he wants to convey to the 
reader. If the understander is to reconstruct 
those ideas, he ought to know why the particular 
metaphor was ChOSen. This information is 
precisely that which the metaphor conveys that 
is absent from a literal expression of the same 
concept. (E.g.. "John is completely crazy about 
Mary" vs. "John loves mary very much". The 
former implies that John may exhibit impulsive 
or uncharacteristic behavior, and that his 
present state of mind may be less permanent 
than in the latter case. Such information ought 
to be stored with the love-is-madness metaphor 
unless the understanding system is sufficiently 
sophisticated to make these inferences by other 
means.) 
• A Transfer Maooino, analogous to Winston's 
Transfer Frames \[10\], is a filter that determines 
which additional Darts of the literal input may be 
mapDed onto the conceptual representation, 
and establishes exactly the transformation that 
this additional information must undergo. 
Hence, in "Prices are soaring", we need to use 
the basic maDDing of the more-is.up metaphor 
to understand that prices are increasing, and 
we must use the transfer map of the same 
metaphor to interpret "soar" ( = rising high and 
fast) as large increases that are happening fast. 
For this metaphor, altitude descriptors map into 
corresponding Quantit~ descriptors and rate 
descriptors remain unchanged. This information 
is part of the transfer maDDing. In general, the 
default assumption is that all descriptors remain 
unchanged unless specified otherwise - hence, 
the frame problem {5\] is circumvented. 
4. A Glimpse into the Process Model 
The information encoded in the general metaphors must be 
brought to bear in the understanding process. Here, 1 outli,'q 
the most direct way to extract maximal utility from the 
general.metaphor information. Perhaps a more subtle 
process that integrates metaphor information more closely 
w h other conceptual knowledge iS required. An attempt to 
implement this method in the near future will serve as a 
pragmatic measure of its soundness. 
The general process for applying metaphor-mapping 
knowledge is the following: 
18 
1. Attempt to analyze the input utterance in a 
literal, conventional fashion. If this fails, and the 
failure is caused by a semantic cese-constraint 
violation, go to the next step. (Otherwise, the 
failure is probably not due to the presence of a 
metaphor.) 
2. Apply the recognition networks of the 
generalized metaphors. If on e succeeds, then 
retrieve all the information stored with that 
metaphorical maDDing and go on to the next 
step. (Otherwise, we have an unknown 
metaphor or a different failure in the originai 
semantic interpretation. Store this case for 
future evaluation by the system builder.) 
3. Use the basic maDDing to establish the semantic 
framework of the input utterance. 
4. Use the transfer maDDing to fill the slots of the 
meaning framework with the entities in the 
input, transforming them as specified in the 
transfer map. If any inconsistenc=es arise in the 
meaning framework, either the wrong metaphor 
was chosen, or there is a second metaphor in 
the input (or the input is meaningless). 
5. Integrate into the semantic framework any 
additional information found in the 
implicit-intention component that does not 
contradict existing information. 
6. Remember this instantiation of the general 
metaphor within the scope of the present dialog 
(or text). It is likely that the same metaphor will 
be used again with the same transfer mappings 
present but with additional information 
conveyed. (Often one participant in a dialog 
"picks up" the metaphors used by by the other 
participant. Moreover, some metaphors can 
serve to structure an entire conversation.) 
5. Two Examples Brought to Light 
Let us see how to apply the metaphor interpretation method 
to some newspaper headlines that rely on complex 
metaphors. Consider the following example from the New 
York Times: 
Speculators brace for a crash in the soaring 
gold market. 
Can gold soar? Can a market soar? Certainly not by any 
literal interpretation. A language interpreter could initiate a 
complex heuristic search (or simply an exhaustive search) to 
determine the most likely ways that "soaring" could modify 
gold or gold markets. For instance, one can conceive of a 
spreading.activation search starting from the semantic 
network nodes for "gold market" and "soar" (assuming 
such nodes exist in the memory) to determine the 
minimal.path intersections, much like Quillian originally 
proposed {7\]. However, this mindless intersection search is 
not only extremely inefficient, but will invariably yield wrong 
answers. (E.g., a golcl market ISA market, and a market can 
sell fireworks that soar through the sky - to suggest a totally 
spurious connection.) A system absolutely requires 
knowledge of the mappings in the more-is.ul~ metaphor to 
establish the appropriate and only the appropriate 
connection. 
In comparison, consider an application of the general 
mechanism described in the previous section to the 
"soaring gold market" example. Upon realizing that a literaJ 
interpretation fails, the system can take the most salient 
semantic features of "soaring" and "gold markets" and 
apply them to the recognition networks of the generaJ 
metaphors. Thus, "upward movement" from soaring 
matches "up" in the more-is.up metaphor, while "increase 
in value or volume" of "gold markets" matches the "more" 
side of the metaphor. The recognition of our example as an 
instance of the general more-is-up metaphor establishes its 
basic meaning. It is crucial to note that without knowledge 
that the concept up (or ascents) may map to more (or 
increases), there appears to be no general tractable 
mechanism for semantic interpretation of our example. 
The transfer map embellishes the original semantic 
framework of a gold market whose value is increasing. 
Namely, "soaring" establishes that the increase is rapid and 
not firmly supported. (A soaring object may come tumbling 
down -> rapid increases in value may be followed by equally 
rapid decreases). Some inferences that are true of things 
that soar can also transfer: If a soaring object tumbles it may 
undergo a significant negative state change -> the gold 
market (and those who ride it) may suffer significant 
neaative state chan.qes. However, physical states map onto 
financial states. 
The less-is-down half of the metaphor is, of course, also 
useful in this example, as we saw in the preceding 
discussion. Moreover. this half of the metaphor is crucial to 
understand the phrase "bracing for a crash". This phrase 
must pass through the transfer map to make sense in the 
financial gold market world. In fact. it passes through very 
easily. Recalling that physical states map to financial states, 
"bracing" maps from "preparing for an expected sudden 
physical state change" to "preparing for a sudden financial 
state change". "Crash" refers directly to the cause of the 
negative physical state change, and it is mapped onto an 
analogous cause of the financial state change. 
More-is-up. less-is-down is such a ubiquitous metaphor that 
there are probably no specific intentions conveyed by the 
writer in his choice of the metaphor (unlike the 
love-is-madness metaphor). The instantiation of this 
metaphor should be remembered in interpreting subsequent 
text. For instance, had our example continued: 
Analysts expect gold prices to hit bottom 
soon, but investors may be in for a 
harrowing roller-coaster ride. 
We would have needed the context of: "uP means increaSes 
in the gold market, and clown means decreases in the same 
market, which can severely affect investors" before we 
could hope to understand the "roller-coaster ride" as 
"unpredictable increases and decreases suffered by 
speculators and investors". 
Consider briefly a Second example: 
Press Censorship is a barrier to free 
communication. 
I have used this example before to illustrate the difficulty in 
interpreting the meaning of the word "barrier". A barrier is a 
physical object that disenables physical motion through its 
Location (e.g., "The fallen tree is a barrier to traffic"). 
Previously I proposed a semantic relaxation method to 
understand an "information transfer" barrier. However, 
there is a more elegant solution based on the conduit 
metaphor. The press is a conduit for communication. (Ideas 
have been packaged into words in newspaper articles and 
must now be distributed along the mass media conduit.) A 
barrier can be interpreted as a physical blockage of this 
conduit thereby disenabling the dissemination of information 
as packaged ideas, The benefits of applying the conduit 
metaphor is that only the original "physical object" meaning 
of barrier is required by the understanding system. In 
addition, the retention of the basic meaning of barrier (rather 
than some vague abstraction thereof) enables a language 
understander to interpret sentences like "The censorship 
barriers were lifted by the new regime." Had we relaxed the 
requirement that a barrier be a physical object, it would be 
difficult to interpret what it means to "lift" an abstract 
disenablement entity. On the other hand, the lifting of a 
physical object implies that its function as a disenabler of 
physical transfer no longer applies; therefore, the conduit is 
again open, a~nd free communication can proceed. 
In both our examples the interpretation of a metaphor to 
understand one sentence helped considerably in 
unaerstanding a subsequent sentence that retered to the 
metaphorical mapping established earlier. Hence, the 
significance of metaphor interpretation for understanding 
coherent text or dialog can hardly be overestimated, 
Metaphors often span several sentences and may structure 
the entire text around a particular metaphorical mapping (or 
a more explicit analogy) that helps convey the writer's 
central theme or idea. A future area of investigation for this 
writer will focus on the use of metaphors and analogy to root 
new ideas on old concepts and thereby convey them in a 
more natural and comprehensible manner. If metaphors and 
analogies help humans understand new concepts by 
relating them to existing knowledge, perhaps metaphors and 
analogies should also be instrumental in computer models 
that strive to interpret new conceptual information. 
19 
6. Freezing and Packaging Metaphors 
We have seen how the recognition of basic general 
metaphors greatly structures and facilitates the 
understanding process. However, there are many problems 
in understanding metaphors and analogies that we have not 
yet addressed. For instance, we have said little about 
explicit analogies found in text. I believe the computational 
process used in understanding analogies to be the same as 
that used in understanding metaphors, The difference is 
one of recognition and universality of acceptance in the 
underlying mappings. That is, an analogy makes the basic 
mapping explicit (sometimes the additional transfer maps 
are also detailed), whereas in a metaphor the mapping must 
be recognized (or reconstructed) by the understander. 
However, the general metaphor mappings are already 
known to the understander - he need only recognize them 
and instantiate them. Analogical mappings are usually new 
mappings, not necessarily known to the understander. 
Therefore, such mappings must be spelled out (in 
establishing the analogy) before they can be used. If a 
maDDing is often used as an analogy it may become an 
accepted metaphor; the explanatory recluirement is 
Suppressed if the speaker believes his listener has become 
familiar with the maDDing. 
This suggests one method of learning new metaphors. A 
maDDing abstracted from the interpretation of several 
analogies can become packaged into a metaphor definition. 
The corTesDonding subparts of the analogy will form the 
transfer map, if they are consistent across the various 
analogy instances. The recognition network can be formed 
by noting the specific semantic features whose presence 
was required each time the analogy was stated and those 
that were necessarily refered to after the statement of the 
analogy. The most difficult Dart to learn is the intentional 
component. The understander would need to know or have 
inferred the writer's intentions at the time he expressed the 
analogy. 
Two other issues we have not yet addressed are: Not all 
metaphors are instantiations of a small set of generalized 
metaphor mappings. Many metaphors appear to become 
frozen in the language, either packaged into phrases with 
fixed meaning (e.g., "prices are going through the roof", an 
instance of the more-is-up metaphor), or more specialized 
entities than the generalized mappings, but not as specific 
as fixed phrases. I set the former issue aside remarkino that 
if a small set of general constructs can account for the bulk 
of a complex phenomenon, then they merit an in-depth 
investigation. Other metaphors may simpty be less-often 
encountered mappings. The latter issue, however, requires 
further discussion. 
I propose that typical instantiations of generalized 
metaphors be recognized and remembered as part of the 
metaphor interpretation process. These instantiations will 
serve to grow a hierarchy of often.encountered 
metaphorical mappings from the top down. That is, typical 
specializations of generalized metaphors are stored in a 
specialization hierarchy (similar to a semantic network, with 
ISA inheritance pointers to the generalized concept of which 
they are specializations). These typical instanceS can in turn 
spawn more specific instantiations (if encountered with 
sufficient frequency in the language analysis), and the 
process can continue until until the fixed-phrase level is 
reached. Clearly. growing all possible specializations of a 
generalized maDDing is prohibitive in space, and the vast 
majority of the specializations thus generated would never 
be encountered in processing language. The sparseness of 
typical instantiations is the key to saving space. Only those 
instantiations of more general me. ~ohors that are repeatedly 
encountered are assimilated into t, Je hieraruhy. Moreover, 
the number or frequency of reclui=ed instances before 
assimilation takes place is a parameter that can be set 
according to the requirements of the system builder (or 
user). In this fashion, commonly-encountered metaphors will 
be recognized and understood much faster than more 
obscure instantiations of the general metaphors. 
It is important to note that creating new instantiations of 
more general mappings is a much simpler process than 
generalizing existing concepts. Therefore, this type of 
specialization-based learning ought to be Quite tractable 
with current technology. 
7. Wrapping Up 
The ideas described in this paper have not yet been 
implemented in a functioning computer system. I hope to 
start incorpor,3ting them into the POLITICS parser \[2\], which 
is modelled after Riesbeck's rule.based ELI \[8\]. 
The philosophy underlying this work is that Computational 
Linguistics and Artificial Intelligence can take full advantage 
of - not merely tolerate or circumvent - metaphors used 
extensively in natural language, in case the reader is still in 
doubt about the necessity to analyze metaphor as an 
integral Dart of any comprehensive natural language system, 
I point out that that there are over 100 metaphors in the 
above text, not counting the examples. To illustrate further 
the ubiquity of metaphor and the difficulty we sometimes 
have in realizing its presence, I note that each section 
header and the title of this PaDer contain undeniable 
metaphors. 
8. References 
1. Carbonell, J. G., "Towards a Self.Extending Parser," 
Proceedings of the 17th Meeting of the Association 
for Computational Linguistics. 1979, PD- 3-7. 
2. Carbonell, J.G., "POLITICS: An Experiment in 
Subjective Understanding and Integrated 
Reasoning," in Inside Computer Understanding: 
Five Programs Plus Miniatures, R. C. Schank and 
C. K. RiesPeck, ecls., New Jersey: Erlbaum, 1980. 
3. Hobbs, J.R., "Metaphor, Metaphor Schemata, and 
Selective Inference," Tech. report 204, SRi 
International, 1979. 
4. Lakoff, G. and Johnson, M., Metaphors We Live By. 
Chicago University Press, 1980. 
5. McCarthy, J. and Hayes, P.J., "Some Philosophical 
Problems from Artificial Intelligence," in Machine 
Intelligence 6, Meltzer and Michie, eds., Edinburgh 
University Press, 1969. 
6. Ortony, A., "Metaphor," in Theoretical Issues in 
Reading Comprehension, R. Spire et aL eds., 
Hillsdale, NJ: Erlbaum, 1980. 
7. Ouillian, M.R., "Semantic Memory," in Semantic 
Information Processing. Minsky, M., ed., MIT Press, 
1968. 
8. Riesbeck, C. and Schank, R. C., "Comprehension by 
Computer: Expectation-Based Analysis of Sentences 
in Context," Tech. report78, Computer Science 
Department, Yale University, 1976. 
20 
9, 
10. 
Wilks. Y., "Knowledge Structures and Language 
Boundaries," Proceedings of the Fifth /nternational 
Joint Conference on Artificial/ntel/igence. 1977, pp. 
151-157. 
Winston, P., "Learning by Creating and Justifying 
Transfer Frames," Tech. report AIM-520, AI 
Laboratory. M.I.T., Jan. 1978. 
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