Understanding Exploitations of Familiar Conceptual Metaphors: An Approach and Artificial Intelligence System John A. Barnden and Mark G. Lee School of Computer Science, The University of Birmingham Birmingham, B15 2TT, United Kingdom
[email protected] March 31, 1999
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Abstract We present an approach to the reasoning needed to handle a class of metaphorical utterances, and a computer program (ATT-Meta) implementing that approach. The approach emanates from artificial intelligence research but is offered also for consideration by cognitive scientists generally. The class includes utterances that exploit familiar conceptual metaphors by going beyond the between-domain mappings they involve. The crux in dealing with such utterances is possibly-extensive inferencing in the terms of the source (vehicle) domain of the conceptual metaphor, and in, generally, avoiding attempts to find or create a targetdomain correspondent for a source-domain concept used in the utterance but not yet mapped to the target by the metaphor. The approach is similar in flavor to those of a small number of other metaphor researchers, but we present new claims and a more extensive implementation. Significant claims arise from our close attention to reasoning uncertainty; notably, we take a distinctive stance on conflicts between metaphorical inferences and target-domain information.
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1 Introduction Consider the sentence (1) “The government erected a barrier to the free communication of ideas,” which is a variant of one discussed by Carbonell (1982). We take it to rest upon a metaphorical view in which ideas are cast as physical objects, and communication is cast as travel of these physical objects between people. This view is part of the “CONDUIT metaphor” (Reddy 1979/1993), which we assume to be familiar to ordinary English speakers. By a “metaphorical view” we mean a “conceptual metaphor” in the sense of Lakoff (1993), but we prefer the former term. A metaphorical view involves mappings between some set of notions in the source domain (here, the domain of physical objects and physical space) to notions in the target domain (idea-communication). We say that those source-domain notions are map-enshrined. The question is, is the notion of a physical barrier map-enshrined—that is, does the view map that notion to some abstract notion of idea-communicationdisenabler? And is the notion, used in (1), of erecting a physical barrier map-enshrined? What about assembling, constructing, taking-apart, knocking down, tunneling-through a barrier? The English words for these notions could all be used in metaphorical utterances about communication. If the notions are not map-enshrined, then we have to produce an account of how they are to be handled by understanders of the utterances. For instance, should the understander now try to construct a mapping for the map-enshrined notions used in the utterance? (Our answer is: typically, no.) The problem is difficult, because it seems inconceivable that more than a small minority of notions in the source domain of any given metaphorical view could be map-enshrined, and there seems to be no identifiable limit to metaphorically applying the language we use for talking about physical objects and movement to the idea-communication domain. Ideas can “spread” from place to place, can “seep” through a barrier, can “bounce” back and forth, and so on; and any way of expressing the notion that a physical barrier is created to block the movement of ideas-qua-physical-objects will make the utterance have the target-domain connotation that something was introduced to inhibit idea-communication. This productive use of language from one domain to talk about another is well known from the work of Carbonell, Reddy, Lakoff, Gibbs and others (Carbonell, 1982; Gibbs, 1998; Lakoff, 1993; Lakoff & Turner 1989; Lakoff & Johnson, 1980; Martin, 1990, 1994; Reddy, 1979/1993; but see Croft, 1998, and Gibbs, 1998, on the difficulty of choosing between possibilities for accounting for the productivity). In the area of corpus studies of metaphor, Deignan (1999) has said that “speakers regularly exploit and extend existing metaphors as a way of creating new meanings” and “It seems that virtually any source domain collocate of a word that has an established metaphorical sense can be extended metaphorically into the same target domain.” So, it is crucial to provide an approach to handling non-map-enshrined notions in metaphorical utterances, even when the metaphorical view itself is already familiar. We focus in this article entirely on metaphorical views that are familiar to the understander of the utterances in question. We say that an utterance that uses a (familiar) metaphorical view and appeals to some non-map-enshrined notions in its source 3
domain exploits the view. Our notion of exploitation is more neutral than that of some metaphor researchers. For example, Moon (1998) uses it to mean a case where a special stylistic effect is sought by the speaker or writer. Our “exploitation” might be called “extension” by some researchers, but we prefer to avoid that term for reasons that will become apparent. Note also that exploitation in a metaphorical utterance does not have to be novel to an understander: the understander may have previously encountered the word usages concerned but still not have constructed source-to-target mappings for the underlying notions. The above considerations do not commit to all source-domain notions in a given metaphorical view being exploitable, but only to the set being (very) large and open-ended. As regards the gaps (the unexploitable source notions), Clausner & Croft (1997) and Grady (1997) point out that by suitably refining one’s theoretical view of what metaphorical views are actually involved in utterances, one can go a long way towards eliminating gaps. This article provides an approach to exploitative metaphorical utterances and presents a computer implementation of it. The approach and implementation have previously been sketched in (Barnden, 1992, 1998a,b; Barnden et al., 1994a,b, 1996), but the present paper presents a more extensive, up-to-date account, with various new claims. The basics of the approach are not novel, as they can be traced back to brief comments in Carbonell (1982) and to the approach of Hobbs (1990) to familiar metaphorical views. Narayanan (1997) has also recently adopted an approach broadly similar to ours, and other somewhat related work has been done by Martin (1990) and Veale & Keane (1992). However, Hobbs’ work on metaphor has apparently not yet been implemented in a working system (Hobbs, personal communication, 1998). Compared to Narayanan’s, Martin’s and Veale’s our work has been more general-purpose, allows more elaborate reasoning and and has addressed issues of uncertainty more intensively (while those authors has grappled with issues we have ignored). Also, there are additional major claims we make that those authors (or indeed any others) appear not to have made, or, certainly, have not argued for. Apart from the AI researchers just cited, metaphor researchers have not provided detailed accounts of how exploitative metaphorical utterances are to be handled, though the phenomenon of exploitation itself is often alluded to. Other contemporary work in AI on metaphor (e.g. Fass, 1997; Iverson & Helmreich, 1992; Sun, 1995; Veale & Keane, 1997) is largely about working out mappings rather than exploiting them. In Psychology, no approach to exploitation is provided by the dominant approaches such as SME (Falkenhainer et al., 1989; Gentner, 1983), ACME (Holyoak & Thagard, 1989; Holyoak et al., 1994), IAM (Keane, 1988), feature-transfer approaches (e.g., Ortony, 1979) and the categorization approach of Glucksberg & Keysar (1990). None of these address exploitation in our sense, and all except the categorization approach are fundamentally to do with working out the mappings or feature-transfers for metaphorical views from scratch, thus treating them as unfamiliar. Yet, we suspect Boers (1997) is right to suggest that most novel figurative language is the result of taking established metaphorical views a little further [i.e., exploiting them], rather than using newly invented metaphorical views. By contrast to the focus of the metaphor field on mapping-creation, our approach goes in the direction of avoiding such creation as far as possible during understanding. One of our main points is that exploitation of
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the metaphorical view is not a matter of extending the analogy, to map non-map-enshrined notions. Instead, the hub of our account is on-the-fly source-based reasoning. This is reasoning conducted in the terms of the source domain in an effort to link non-map-enshrined notions in the utterance to map-enshrined ones, so that only the mappings of the latter are used. This is the relevant basic insight of Carbonell (1982). Our implemented system is not intended as a psychological model but primarily as a contribution to AI. However, we do tentatively put forward our high-level claims and overall processing strategy for consideration by psychologists, philosophers, cognitive linguists, and so on. One point of potential dispute is as follows. The approach is centered on constructing source-domain meanings (roughly, “literal” meanings) of metaphorical utterances or major constituents of them. The source-domain meaning of (1) is that the government physically erected some physical barrier to the physical flow of ideas that are themselves physical objects. The approach might therefore appear to conflict with some experimental results that have been put forward in support of the idea that literal meanings may not be computed during understanding of metaphorical utterances, because the utterances take no longer to understand than literal ones under certain conditions. For review and discussion of these results, see Gerrig (1989), Gibbs (1989, 1990, 1998), Gineste & Scart-Lhomme (1998), Lytinen et al. (1992), Onishi & Murphy (1993), and R´ecanati (1993). However, some recent work (Giora, 1997; Honeck et al., 1998) throws serious doubt on the claim that literal meanings are avoided, and in any case it has been pointed out (Gibbs et al. 1996, R´ecanati, 1993) that the claim cannot really be that the literal meanings of metaphorically-used words are not used, but only that a literal meaning for a whole sentence is not constructed. So really the dispute is about how big the syntactic units are that get literal treatment. Our approach does not actually claim that whole metaphorical sentences must get literal meanings, although this is what happens in the examples in this article. There is no space in this paper for further treatment of these issues. Further brief comments can be found in Barnden (1998a,b). A particular area for concern for us, but one largely put aside in other work on metaphor, is the detailed computational effects of bringing uncertainty of reasoning into the metaphor processing picture. One effect is that our approach embodies further marked departures from other treatments of metaphor, for instance on the question of override relationships been target information and source-derived information. The plan of the paper is as follows. Section 2 describes and illustrates the approach we advocate to the handling of exploitative metaphorical utterances. Section 3 sketches how the approach is instantiated, in large measure, in our implemented metaphorical-reasoning system, ATT-Meta, and also provides a more detailed example of the general approach in action. Section 4 engages in further discussion, and Section 5 concludes.
2 The Advocated Approach We proceed by first giving an initial account of our main claims, then presenting two examples in some detail to flesh out those claims, and finally addressing some further issues.
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A word on our terminology and our overall stance on metaphoricity. We say that metaphorical (parts of) utterances are manifestations of metaphorical views. But the matter is relative to the particular understander. The sentence “the idea pounced out at him” is analyzable (by us) as a manifestation of a metaphorical view of ideas as animate creatures for an understander insofar as the information conveyed by the sentence, in context, to the understander can at least be theoretically motivated by, and perhaps actually derived from, the animate-creature meaning of “pounce,” with the help of source-to-target mappings that the understander takes to be involved in the metaphorical view. Also, an utterance does not have to be consciously recognized by the understander as metaphorical, least of all as a manifestation of a particular metaphorical view, to count as a manifestation of that view for that understander, and the understander does not have to have conscious knowledge of the mappings he, she or it takes to be involved in the view.
2.1 Main Claims on Overall Processing Strategy Our approach can be seen as a major extension and elaboration of brief comments by Carbonell (1982). Our example (1) was derived from the following example discussed by Carbonell: (2) “Press censorship is a barrier to free communication” Carbonell does not assume that the notion of a physical barrier is map-enshrined for the understander. Carbonell points out that the understander needs only a physical-barrier meaning for the word “barrier” to understand the above sentence to be saying that press censorship disenables communication—the understander does not need (a) to know some more general, abstract notion of barrier for which a physical barrier and a an idea-communication barrier are special cases. Equally, we may add that the understander does not need (b) to know a mapping from the special notion of a physical barrier to an already-known special notion of an idea-communication disenabler, and does not need (c) to have a special lexical entry under “barrier” for the latter notion. Nor is there any need, simply from the point of view of understanding the sentence at hand, (d) to construct a new barrier notion in the idea-communication domain. We are not saying that the understander should never do (a) to (d), but rather that it is wrong to assume that the understander must do any of these things. Instead, following Carbonell, the proposal is that the understander should notice that a physical barrier disenables whatever (physical) process it is a barrier to. In the example, that process is the physical movement of ideas through the press-as-physical-conduit. Then, as long as disenablement maps over from source domain to target domain, the understander can infer disenablement of the abstract process of communication in the target domain. Disenablement does map over in Carbonell’s approach, because of a general mapping strategy discussed below (section 2.3). We generalize and codify these observations in the following three general claims, which are closely related to each other: (3) exploitation of a metaphorical view often, or even typically, does not imply extension of the mappings involved in the view.
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(4) Exploitation can typically rely merely on the source-domain meanings of terms in a metaphorical utterance, as opposed to relying on a mapping of those meanings into the target domain or on the possession of a more abstract meaning spanning both source and target. (5) That reliance consists in proceeding by an indefinite amount of on-the-fly source-based inference: inference, within the terms of the source domain, to infer propositions that involve only map-enshrined source notions from propositions that involve non-map-enshrined source notions used in the utterance, and then using the existing mappings believed in by the understander to transfer the inferred propositions into the target domain. In fact, (5) is oversimplified, as our approach allows target-domain reasoning to be mixed in with the sourcebased reasoning. This is discussed in section 3.7.10. We would imagine that on-the-fly source-based inference could be a compatible addition to many current accounts of metaphor, and indeed inference of this type is occasionally briefly discussed (e.g. in: Fauconnier & Turner, 1998; Lakoff, 1993). Also, the type of inferencing we have in mind is related to elaborative reasoning within source cases as commonly proposed in the field of case-based reasoning (Kolodner, 1993). Metaphor researchers sometimes state that patterns of inference are mapped from the source domain to the target domain (Black 1979/1993, Lakoff & Turner 1989). However, these claims are usually vague from an algorithmic point of view, and typically it is not clear how much source-based inference is envisaged, or of what type, or to what extent it is performed on-the-fly during understanding. In any case, we make another important claim: (6) source inference patterns that are central in metaphorical interpretation may very well not have, or need, target inference patterns parallel to them. Patterns of source inference are primarily important for the propositions they produce in terms of the source domain, not for whether they are structurally similar to patterns in the target. We are not saying that they cannot have that relationship to the target — but only that that is a special case. They may make the metaphorical view more apt than it would otherwise be, but aptness is not an issue that we currently address. Our approach contrasts with what we will call the “mainstream” analogy-based view of how to perform metaphorical source-target transfers (projections, mappings of as-yet-unmapped items). The mainstream view is typified by SME, ACME and IAM (even though there are important differences between these approaches). We do hold that metaphor is based on analogy. The analogy consists in the set of mappings believed in by the understander. However, we see a crucially different way in which analogy is actually used in the process of generating source-to-target transfers. The mainstream view has it that particular propositions that are transferred should be suitably linked to the structure of source propositions, describing a particular situation, that already have a detailed, item-by-item structural mapping to the target domain. But we claim that this is not appropriate in the case of exploitation of familiar metaphorical views. Instead: 7
(7) the warrant for transfer is linkage to source domain propositions that are conveyed by the metaphorical utterance (about a particular source-domain situation) but that typically are not themselves either already mapped or now to be transferred during the processing of the current metaphorical input. (8) propositions transferred from source to target may also rely heavily on general knowledge about the source domain that, again, is itself not already mapped or now to be transferred. Although our approach rests to a large extent on Lakoff’s notion of conceptual metaphor, he has made claims that conflict with (6), (7) and (8). For instance, in Lakoff (1993) he discusses the sentence “We’re driving in the fast lane on the freeway of love” as a manifestation of the metaphorical view of LOVE AS A JOURNEY. On p.211 he says that the knowledge structures associated with the words “freeway” and “fast lane,” which express non-map-enshrined notions, are given a mapping to the target during understanding. We are opposed to this. Similarly, we are opposed to Grady’s (1997) claim that when a sentence uses what we would call a non-map-enshrined source notion, the understander should somehow bring in a further metaphorical view that does map the notion. On the other hand, our tendency to avoid mapping non-map-enshrined source items is broadly similar to the selective-projection stance of Fauconnier & Turner (1998). However, that work does not provide a precise method for selection of what to project from one space to another.
2.2 Main Claims concerning Uncertainty Our detailed attention to uncertainty of reasoning departs from almost all other work on metaphor. Our basic observation on uncertainty is: (9) In practice, the reasoning needed within target and source domains during metaphorical understanding is usually heavily laden with uncertainty. For example, that physical barriers disenable the physical processes they are barriers to is at best a default principle within the domain of physical objects and processes, not a certainty. Once within-domain uncertainty and other types of uncertainty are brought in properly, it affects various issues that are salient in the metaphor literature, such as the issue of conflict between, on the one hand, transfers from source to target and, on the other hand, information already available about the target. Some authors imply that the latter information should generally have precedence. For example, the Invariance Principle in Lakoff (1993) allows transfer of “image-schematic structure” from source to target but only if this does not clash with target structure. Fauconnier & Turner (1998) make similar but less restrictive claims. They appear to allow clashes in some cases and to allow the source information to win sometimes. There is no account, however, of when such clashes are to be allowed or how they are to be adjudicated. In any case, we argue that (10) the direction of precedence depends on the particular situation at hand, and often it is the metaphor-based inferences that should win. 8
Of course, when the non-metaphorically-derived target information in question is absolutely certain then it should win, but our point is precisely that the target information will often, if not usually, be uncertain, and there is no reason to think that the uncertainty is less than that inherently involved in metaphor-based reasoning. Metaphorical utterances often act to defeat within-target defaults that one would otherwise normally respect. Furthermore, (11) the question of whether a metaphorical-derived inference should or should not defeat a within-target inference can and should be sorted out by general-purpose conflict-resolution techniques, not by means of special defeat principles concerned specifically with metaphor. Also, as soon as uncertainty is properly considered it becomes apparent that, (12) the analogical mappings in a metaphorical view can themselves be intrinsically uncertain, and are potentially subject to defeat by the direct or indirect effect of other mappings involved in the very same metaphorical view. This should be no more surprising that that different pieces of information within any given domain can conflict with each other: notably, one piece can be more specific and therefore stand to override another. The rule that birds fly, by default, can be overridden by the rule that penguins do not. A similar thing applies to metaphorical mappings. For example, in a view of ORGANIZATIONS AS PHYSICAL OBJECTS, eating actions by companies might be mapped to legal company-acquisition be default, but there could be a more specific mapping that says that eating by a Mafia-run company maps (by default) to illegal company acquisition.
2.3 Claim concerning General Qualities of Events A further feature of Carbonell’s suggestions that we subscribe to is that (13) general features of events, processes, etc. in a metaphor source domain do tend to get carried over to the target. For example, if some entity E enables or disenables a process P in the source domain, where that entity and process correspond to an entity E 0 and process P0 in the target domain, then the proposition that E0 enables P0 or disenables P0 (respectively) is a candidate metaphorical inference. Similarly for temporal, causal, part-of, and other properties of and relationships among events and processes (e.g., the rapidity of a process). This is consistent with the notion of a pervasive EVENT-STRUCTURE metaphorical view as discussed by Lakoff (1993). We now move to a detailed consideration of how our approach would apply to two examples. A third example will be covered in section 3. We do not continue with the barrier example because we wish to present other examples that better illustrate some of our claims. 9
2.4 Gobbling-Company Example Consider the sentence (14) “The company gobbled up its competitor.” We assume this manifests a metaphorical view of ORGANIZATION AS PHYSICAL OBJECT to the understander. References to companies gobbling things are quite common in real discourse, judging by the occurrences we found in the British National Corpus. This might suggest that it would be worthwhile for an understander to have a special lexical entry for a commercial meaning of gobbling, or for the physical notion of gobbling to be map-enshrined in ORGANIZATION AS PHYSICAL OBJECT. However, we will as for illustration of our approach that neither of these possibilities holds. Even if one of them did, variants of (14) could require our approach (see the discussion in section 4.6). The primary connotations of the sentence are, presumably, that the company (a) rapidly acquired the resources (money, employees, equipment) of its competitor and (b) caused the competitor to cease to exist as an entity outside the company. We concentrate on (a). We assume that the understander believes in the following analogical mappings for ORGANIZATION AS PHYSICAL OBJECT: For organizations O that are being viewed as physical objects:
being a set of organizational resources of O corresponds to being a physical substance within O; for organizational resource-sets viewed as physical substances:
the subset relation corresponds to the physically-within relation. Figure 1 shows the advocated process of inferring connotation (a), using the above analogical correspondences and some illustrative source-based reasoning. Also, the reasoning involves extra steps, consisting purely of target-based reasoning. The source-based reasoning relies on the common-sense knowledge fragments that gobbling is a type of rapid eating and that if an animal eats something then it causes the substance of that thing to come to be included in the substance of the animal. The source-based reasoning takes place in a special computational environment called the “source-based pretence cocoon.” We use this name to stress the fact that the understander is effectively pretending, in the current example, that the company and competitor are literally an animal and a food item, while not of course really believing this to be the case. Note that it is the company and competitor themselves, rather than mere hypothetical analogical correspondents of them, that are held to be an animal and a food item, respectively, within the cocoon. One reason for taking this line will be discussed in Section 4.2. The example mainly illustrates claims (3) to (8) inclusive and (13). Claim (13) is used in the transfer step shown in the Figure: we have assumed that changes map to changes and that rapidity maps over as well. We have shown that one very important connotation of the sentence can be drawn without having to extend any existing analogical mappings to deal directly with gobbling. Nor do we transfer to the target the pattern
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of inference occurring within the pretence cocoon, or the intermediate notions used within that pattern. The inference within the cocoon, which is partially uncertain, is just a tool for linking the source-based meaning of the sentence to a proposition (or a set of propositions) that already-known analogical mappings can work on. In the example, that proposition is that the company physically absorbed the substance of the competitor. This proposition is transferred to the target, to become the proposition that the company acquired the competitor’s resources. The mapping rules used in this example could also deal with many other examples involving not only eating-related actions such as “swallowing” and “spitting out” but also non-eating-related actions such as “merging,” “engulfing” and so forth. For other examples, further mapping rules might be needed, but we claim that with a small set of mapping rules the understander has tremendous power for dealing with nonmap-enshrined manifestations of the metaphorical view in question. We conjecture that for any given mundane metaphorical view the set of analogical mapping rules that needs to be known is quite small—to be counted in ones or a few tens rather than hundreds or higher. We provide some support for this in section 4.1. As an example of a further powerful mapping rule for ORGANIZATIONS AS PHYSICAL OBJECTS, there could be one that maps PHYSICAL OPERATION to abstract ORGANIZATIONAL OPERATION. This, in conjunction with the mappings implied by claim (13), would cope with utterances that cast an organization as a machine and talk about it “running,” “working,” “breaking down,” “running out of fuel,” etc. In the last case, there is no need to worry about a mapping of physical fuel into the target domain: it is enough to infer within the terms of the source domain that the organization will soon cease to be able to PHYSICALLY OPERATE.
2.5 “Shoving” Example Consider the sentence (15) “John shoved the two ideas together,” where it is already clear from context that John had been entertaining the ideas. We assume this sentence manifests an IDEAS AS PHYSICAL OBJECTS metaphorical view to the understander, but the notion of shoving is not map-enshrined for the understander. In contrast to the case of “gobbling,” there is no pressure to wonder whether shoving is map-enshrined. Mental applications of the notion of shoving do not appear to be common in the British National Corpus, and there are no relevant examples in our own mental-metaphor databank (Barnden, n.d.). On the other hand, the sentence is not mundanely unrealistic in view of the richness of physical language used to talk about the mind in mundane discourse. We assume that the understander believes in the following one-way mapping and correspondence as part of IDEAS AS PHYSICAL OBJECTS: (16) Being one of a particular set of ideas apparent from context (e.g., John’s ideas) maps to being a physical object.
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(17) For ideas being viewed as physical objects: Mental operation upon them by an agent X corresponds to physical operation upon them by X. (Here we take physical “operation” to include perception as well as action.) It is plausible to suppose that the understander can draw the following connotation from the sentence: Connotation: The mentioned ideas came to be involved in John’s mind in such a way that John was in a position to mentally operate upon them conjointly—for instance, John was in a position to compare them or to perform an inference that directly relied on both of them. Figure 2 depicts the inference process for arriving at the above connotation. The source-based reasoning uses General, common-sense knowledge about the source domain of physical objects and human interac-
tions with them. General knowledge about ability: specifically, that if an agent does do something then the agent can
do it. The (absurd) source-domain meaning of the sentence, namely that John physically shoved the two
ideas together. With regard to the first item, we need only assume that the understander believes that: Shoving is a type of physical moving. If entities are physically moved then they are physical objects. To physically move something is a way to physically operate upon it. If entities are physically moved together at some time then they are physically together for some time
interval following that. The agent can physically manipulate entities conjointly if and only if they are physically together and
the agent can physically operate upon them individually. The last two of these are, of course, merely defaults. The source-to-target transfer action in the example (see the big arrow at the bottom of the Figure) is warranted by correspondence (17). In using (17) we have assumed that conjointness of action maps over identically from source to target, and that modalities such as ability also map over. This is again on the lines 12
of claim (13). Once again, the example illustrates claims (3) to (8) inclusive. It also strongly illustrates (9), because of the default nature of the conjointness inference within the cocoon. The avoidance of an urge to transfer the notion of shoving to the target domain is not only practically beneficial from the point of view of speeding up and simplifying the interpretation process, but is also virtually forced upon us. It is surely extremely hard (and perhaps impossible) to find a mental correlate for physical shoving. We simply do not know enough about the mind, whether common-sensically or scientifically. This is just a reflection of the notorious unparaphrasability of many metaphorical utterances. But we go further and claim also that there is no reason to think that the speaker of (15) in any way assumes or hopes that the understander will fully understand, in non-metaphorical terms, what “shoving” two ideas together amounts to, or that the speaker him/herself has such an understanding. Rather, the speaker is only trying to lead the understander to connotations such as the one above. In a nutshell, it does not matter what specific sort of mental event corresponds to shoving. Of course, something happened in John, and this unknown something can be taken to be the analogical correspondent of the metaphorical shoving action. To postulate the unknown something is technically to perform a step of analogical transfer, but only nominally so. Clearly the sentence has connotations other than the one discussed. Possibilities are that the change of John’s mental state was deliberate on John’s part and quick. These connotations can be obtained by reasoning in the pretence cocoon that the physical movement was deliberate and quick. Since these are possible qualities of events in general, whether physical, mental or whatever, we assume they map over (by default) in any metaphorical view, because of claim (13). There is no limit in principle to the amount of source-based reasoning that can go on in the cocoon. Equally, the reasoning can use arbitrary knowledge from the source domain. A sentence that merely indirectly implied that Johns shoved the ideas together, rather than directly stating it, would have the same connotation as analyzed here (among others). We believe that correspondence (17) can account for a large, open-ended range of different manifestations of IDEAS AS PHYSICAL OBJECTS. We will discuss this in section 4.1.
3 Implementation of the Approach in ATT-Meta The advocated approach has been to a great extent implemented in an AI system called ATT-Meta (Barnden, 1998a,b,c; Barnden et al., 1994a,b, 1996). This is purely a reasoning system, and does not currently work directly on text or speech. Instead, a user provides logical forms that are meant to couch the source-based (i.e. “literal”) meanings of some input utterances. The system then uses this information to support connotations, in the way sketched in the previous section. Currently, the system is entirely goal-based: the user supplies a particular reasoning goal, and as a result the system may investigate various connotations of the input in an effort to support that goal. An example of this will be explained below. As a special case, a connotation itself can be a goal. We have adopted goal-directedness partly for principled theoretical reasons 13
discussed in section 4.8 below. We recognize, however, that in reality some degree of forwards reasoning from the data, without any particular goal in mind, may be beneficial. The implemented system respects all the claims displayed in Section 2. However, we do not imagine that the system does a full or perfect job on all these fronts. The biggest deficiency is that we have not developed representational syntax and reasoning rules adequate for dealing properly with such matters as modal conditions, aspectual conditions, time course of events, etc., nor therefore with claim (13). However, there is no bar to the addition of the necessary representational and rule machinery. In particular, the system’s representations use a logic that reifies events and is thus well-placed to handle the neglected matters. ATT-Meta has so far largely been applied to metaphorical views of mental states and processes, such as IDEAS AS PHYSICAL OBJECTS. However, neither the general approach nor is instantiation in ATTMeta are in any way restricted to mental-state metaphor. We discuss mental-state metaphor in Barnden (1997), Barnden et al. (1996) and elsewhere, including within our real-discourse mental-metaphor databank (Barnden, n.d.). As well as being able to reason metaphorically about agents’ beliefs and reasoning, ATTMeta has general, non-metaphor-related facilities for reasoning about agents’ beliefs and reasoning. These facilities are beyond the scope of the present paper, but are described in Barnden (1998c) (see also Barnden et al. 1994a for an early version).
3.1 Representation and Rules The information manipulated by ATT-Meta consists of hypotheses and if-then rules. Rules do not change during reasoning, and embody long-term knowledge, such as that IF something is a penguin THEN it cannot fly. One sort of hypothesis is a fact that is given to the system, either a long-term one like the fact that Everest is a mountain or a short-term one specific to a particular situation ATT-Meta is reasoning about. Another type of hypothesis is a reasoning goal that the system is working on, either because the user has supplied it or because the system itself has generated in the reasoning process. As a goal-hypothesis is reasoned about and accumulates support, it becomes more and more proposition-like until it is eventually “finalized” and treated as a proposition established to some final level of certainty. Hypothesis are represented as expressions in a situation-based or episode-based first-order logic (broadly similar in spirit to the logical scheme of Hobbs, 1990). This paper will not display ATT-Meta’s formal representations and formal rule formats (which are in turn implemented as Prolog expressions), and will use English glosses instead. The formalities are not important for the purposes of this paper. Rules use the same style of formal representation, and again the formalities are not important and we will just use English paraphrases. At any time, any particular hypothesis H is tagged with a qualitative certainty level, one of certain, presumed, suggested, possible or certainly-not. The last one just means that the negation of H is certain. Possible just means that the negation of H is not certain but no evidence has yet been found for H itself. Presumed means that H is a default: i.e., it is taken as a working assumption, pending
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further evidence. Suggested means that there is evidence for the hypothesis, but the evidence is not (yet) strong enough to enable H to be a working assumption. When a hypothesis is created (as a goal), it is immediately given a certainty value of possible. Everything is possible until shown not to be. Rule applications can cause the certainty value to rise. Each rule has its own certainty value, which is confined to be suggested, presumed or certain. For instance, one could have the rule IF X is a bird AND NOT(X is dead) THEN [presumed] X can fly. The effect of a rule’s certainty value will be explained in section 3.5. Rules with presumed as their certainty value correspond to default rules in other reasoning systems. As for rules with value certain, these are often taxonomic ones, such as that penguins are birds, in examples we have studied. A rule’s IFpart is usually a conjunction of atomic conditions, or negations of atomic conditions (where atomic means that there are no internal connectives like AND and OR, or use of logical quantification), and the THEN-part is usually an atomic condition or a negation of one.
3.2 Metaphor-Pretence Hypotheses For the purposes of metaphorical pretence, ATT-Meta entertains source-based pretence hypotheses of the form It is pretended that: H with certainty level [at least] . The level can be any of possible, suggested, presumed or certain. Notice that the level of certainty to which the system adheres to a source-based pretence hypothesis is independent of the level within that hypothesis itself: any combination is allowed. The level ATT-Meta attaches to a pretence hypothesis is the level of confidence that it is appropriate to pretend that H has at least the stated level . The contents of the metaphorical pretence cocoons of Section 2 are just the Hs in source-based pretence hypotheses. In the case of dealing with example (15), we would have, for instance, It is pretended that [idea] I1 is a physical object, with certainty level certain. This fact is provided by the user. Currently, such facts are given certainty level certain. However, sourcebased pretence hypotheses that are supported by inference rather than by being given by the user can have lower certain levels. Also, the inner certainty does not have to be certain. Computationally, a source-based pretence hypothesis and its inner hypothesis (H) are two distinct hypotheses, each addressed by reasoning mechanisms. H is within the pretence cocoon, while the source-based pretence hypothesis itself is outside, and they correspondingly can each have their own lines of argument supporting them. However, in addition to this, the two hypotheses interact with each other. In particular, if the pretence hypothesis becomes supported outside to at least level presumed, then H is given at least the level of certainty mentioned in the pretence hypothesis. If the pretence hypothesis is not supported to 15
level presumed, then there is no contribution to H. Conversely, if H is supported within the cocoon to a level 0 , then any metaphorical-pretence hypothesis outside whose internal level is less than or equal to 0 is thereby given a (tentative) level of presumed.
3.3 Conversion Rules In Section 2.5 we had a mapping rule (17) for IDEAS AS PHYSICAL OBJECTS. One ATT-Meta rule we have as part of the effect of (17) is (17a) IF J is an idea AND K is an idea AND X is a person AND it is being pretended that, presumably at least, X can-physically-operate-on fJ,Kg THEN [presumed] X can-mentally-operate-on fJ,Kg. In this rule we finesse the “conjointness” of operation discussed in Section 2.5 by allowing the can-physicallyoperate-on and can-mentally-operate-on relationships to attach to a set of objects rather than just single objects. The mapping between physical operation and mental operation is two-way (it is a correspondence) so we also have a converse rule that maps mental operations to physical operations. However, this rule is not needed in the current example. Also, the system has “contrapositives” of these two rules, going from lack of mental operation on J and K to lack of physical operation on them, and vice versa. This last case is used in the current example and is as follows: (17b) IF J is an idea AND K is an idea AND it is being pretended that, presumably at least, J is a physical-object AND it is being pretended that, presumably at least, K is a physical-object AND X is a person AND it is being pretended that, presumably at least, NOT(X can-physically-operate-on fJ,Kg) THEN [presumed] NOT(X can-mentally-operate-on fJ,Kg). We call (17a), (17b) and their converses “conversion rules.” They are the particular form that mappings take in ATT-Meta. Notice that the converses map from the target domain to the source domain. This is no way compromises the asymmetry of the roles of the target domain and source domain in the metaphorical view
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of IDEAS AS PHYSICAL OBJECTS. The directionality of a metaphorical view governs what mappings are adopted and what target domain it is that is really being reasoned about, not the directionality of mappings.
3.4 Rule Application Goals cause the appearance of other goals through a standard backwards use of rules. For instance, if there is a rule that says that if X is a bird and X is alive then X can fly, and there is a goal to show that Bertie can fly, then the goal that Bertie is a bird is created, and if this receives adequate support the goal that Bertie is alive is created. Suppose that both of these hypotheses are actually given as certain facts by the user. Then the rule provides support for the hypothesis that Bertie can fly, so that this hypothesis is now a proposition that the system to some extent holds to be true. This hypothesis is, however, still goal-like in that it can continue to collect support from other rules. In fact, to oversimplify somewhat with respect to what actually happens, currently all rules are tried on all goals, though of course normally only a minority will having anything to say about any given goal. In future work we intend to introduce optimizations to cut down on the rules that are tried, to supplement some optimizations already used. Hypotheses are annotated as to which “reasoning spaces” they exist in. For now, we need consider only two spaces: the pretence cocoon and the reasoning environment outside the pretence cocoon, which we here call the “top” space. The top space is the system’s own reasoning space, in which the hypotheses are about some target domain. A hypothesis can be replicated in different spaces, but if so the different copies are handled entirely separately, as if they were unrelated hypotheses. This is because even if a hypothesis is supported in two different spaces, the lines of reasoning supporting it can differ radically, and the level of certainty can differ. When a rule is applied to a goal in a specific reasoning space, the goals emanating from its condition part are set up in the same reasoning space. Note therefore that if a condition part has the form of a metaphoricalpretence hypothesis, then we get such a hypothesis in the same reasoning space as the one the goal is in. Thus, the operation of the conversion rules above is entirely within the top space, technically. However (to a first approximation) whenever there is a goal of form “it is pretended that H ...”, then H itself is created as a goal within the pretence cocoon. Conversely (to a first approximation) whenever the reasoning within the pretence cocoon creates a goal H, a hypothesis of form “it is pretended that H ...” is created outside. Because of this correspondence between hypotheses inside and outside the cocoon, conversion rules can be thought of as crossing from inside the cocoon to outside or vice versa.
3.5 Basics of Uncertainty Handling Suppose a rule’s THEN-part matches a goal K that is not a pretence hypothesis and is not in a pretence cocoon. If it turns out that the goals emanating from the rule’s IF-part all get supported to level at least suggested, then the minimum is found of those levels together with the rule’s own level. This minimum is then the rule’s contribution to the certainty of K. In the absence of any other evidence for or against K,
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the level will end up being the one assigned to K. If there are several rule applications contributing to K, then the maximum of the contributions is taken and becomes K’s certainty value unless sufficient evidence accrues against K. If K is a pretence hypothesis or is within the pretence cocoon, then the provisions of the last paragraph apply as modified by the cross-cocoon provisions at the end of the previous subsection.
3.6 Conflict Resolution When a goal is considered by the reasoning engine, the negation of the goal is also often considered. This is the way evidence against a hypothesis can accrue against a (non-certain) hypothesis as well as for it. We now consider various situations concerning the certainty levels emanating from the evidence supporting a hypothesis K and its negation –K. If both hypotheses are supported to level certain, then the system regards itself to be in error and special action is taken (not described here). If only one is supported to level certain, then the other is downgraded to level certainly-not. This is one, simple, form of hypothesis defeat in the system. In all remaining situations, except when both are supported to level presumed, the levels are left as they are. For instance, if one is presumed and the other is suggested, then this simply means that the former is taken as working hypothesis even though the latter has some evidence for it. There is no need to adjust levels or take other special action. The interesting case is when both hypotheses are supported to level presumed. It is in this case that conflict-resolution takes place. The system attempts to see whether one hypothesis has more specific evidence than the other, in a sense to be described in a moment. If one is indeed more specifically supported, it stays presumed and the other is downgraded to suggested. This is another form of hypothesis defeat. If neither hypothesis wins on specificity, both are downgraded to suggested. The system thus takes a conservative approach and refuses to strongly believe either side in an even conflict. The specificity handling is very roughly as follows. All hypotheses are ultimately supported by facts, perhaps via long chains of rule applications. If one of the hypotheses K, –K requires more facts than another, then it is regarded as more specifically supported. This comparison is, in general, complex because there can be several arguments for each of K, –K, using possibly differing sets of facts. If fact support does not decide between K and –K, then inter-derivability relationships between hypotheses appearing in the networks of supporting rule applications are examined. The basic intuition is that if, say, K is establishable on the basis only of the hypotheses used by the conditions in some rule application supporting –K, but –K is not so related to K, then –K is regarded as more specifically supported. The overall method of using the factual basis and inter-derivability relationships is a generalization of the specificity mechanism familiar from the semantic network area (whereby information attached to nodes lower down in a hierarchy has precedence of information attached to nodes higher up) and are strongly related to methods used by some other authors in AI, such as Loui (1987). It is generally recognized that serious problems remain in coming up with adequate and practical heuristics. Our current method is only
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intended as a first approximation to a fully adequate method, but gives reasonable results in many examples.
3.7 Store-Room Example Consider the sentence (18) “The two ideas were in different store-rooms in John’s mind.” ATT-Meta’s handling of this will illustrate all the claims in sections 2.1 to 2.3. The sentence manifests two metaphorical views: MIND AS PHYSICAL SPACE and IDEAS AS PHYSICAL OBJECTS. We assume that the understander is familiar with both metaphorical views. We assume also that the understander only has a physical sense for “store-room” and that the notion of physical store-room is not map-enshrined for the understander. In fact, our use of the notion in this example may be novel: no use of “store-rooms” of minds appears in our mental-metaphor database (Barnden, n.d.) even though the database contains extremely rich and creative examples of the two metaphors mentioned above. In the British National Corpus we could find only one possibly-relevant example: “There must come a point when you realize that all these confidences are piling up, useless, in the store room, ...” (document FAJ 2867). However, the notion of storage more generally is often used in talking about minds, and “storehouses” in the mind are noted in Webster’s dictionary. Therefore, we take (18) to be a plausible candidate for mundane discourse.1 We submit that for most readers, at least when the discourse context is suitable, (18) has the following primary connotation because of the ideas being in different store-rooms: Primary Connotation: The mentioned ideas were involved in John’s mind in such a way that John was NOT in a position to mentally operate upon them conjointly—for instance, to compare them or to perform an inference that directly relied on both of them. Certainly, the sentence can have other connotations, such as that each of the ideas was individually playing a somewhat subsidiary role in John’s mind, because of being packed away in a store-room. However, for illustration, we will concentrate on the above connotation. The desirability of deriving the connotation could arise from discourse-coherence considerations, as explained in Section 3.8. Figure 3 shows the overall reasoning process to be discussed. Much as in the shoving example, the understander can derive important connotations such as the one discussed without trying to transfer the notion of “store-rooms” to the manifestation target, and without having to map or transfer the inference pattern joining the source-based meaning of the sentence to the within-cocoon hypotheses that the known mappings can work on. It is implausible to suggest that anyone knows enough about the mind to be able to identify anything about minds that corresponds to store-rooms. Of course, one could invent an informationprocessing theory of the mind in which there are, say, memory locations that are a good parallel to storerooms; but we would not want to suggest that in ordinary discourse the store-room sentence is intended to 1
“Storage-room” may sound more natural to some speakers than “store-room.” In any case, similar considerations apply.
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be understood in the light of such a theory. Overall, the example illustrates the same claims as the shoving one, though somewhat more strongly in the case of (8), (7) and (9). In particular, (7) is now very strongly in focus. Not only is there no postulation of a mental correspondent for a store-room, but even the sheer fact that there is more than one store-room is not taken to imply that there is in reality more than one identifiable component of John’s mind dealing with the two ideas in question. Thus, not even the grossest aspects of the structure depicted in the source domain are transferred: that structure is merely there to warrant a transfer inferentially related to it. 3.7.1
Conversion Rules in Store-Room Example
In section 3.3 we presented the four conversion rules (17a, 17b and converses) for the physical-operation/mentaloperation correspondence (17) in Section 2.5, which is involved in IDEAS AS PHYSICAL OBJECTS. For the store-rooms example, we add the following conversion rule for MIND AS PHYSICAL SPACE: (19) IF J is an idea AND it is being pretended that, presumably at least, J is physically-in X’s-mind THEN [presumed] X can-mentally-operate-on J. ATT-Meta also has a converse and contrapositive for this rule, and a contrapositive for the converse, but these are not described here as they will not be applied in the example. Rule (19) appears to be intuitively reasonable. The central point about MIND AS PHYSICAL SPACE is that an agent can consider and manipulate ideas that are in their mind-spaces. 3.7.2
Other Metaphor-Related Rules
A matter not so far mentioned is that it can be useful to have, as part of knowledge about a specific metaphorical view, knowledge that refines a mapping involved in that metaphorical view. We include such a rule: (19a) IF X is a person AND it is pretended that: X’s-mind is a physical-region with certainty presumed THEN [presumed] it is pretended that: X can-physically-operate-within X’s-mind, with certainty presumed. This can be thought of as taking the basic mapping from X’s mind to a physical region and refining it by specifying that that region is one in which X can physically operate. We claim that this is a natural part
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of the familiar MIND AS PHYSICAL SPACE metaphorical view. Under this view, the agent is commonly regarded as seeing and manipulating the ideas, etc. located within his or her mind-space. 3.7.3
Rules about Physical Objects and Space
The following are the ATT-Meta rules concerning ordinary physical objects and space that are actually needed for the example. We have simplified matters by not distinguishing between physical containers (e.g. rooms) and physical regions. (20a) IF X is a store-room THEN [certain] X is a room. (20b) IF Y is a store-room AND Y is a part of an entity X THEN [presumed] X is a building. (20c) IF X is a building THEN [certain] X is a physical-region. (20d) IF X is a room THEN [certain] X is a physical-region. (20e) IF an entity X is in Y AND Y is a physical region THEN [presumed] X is a physical object. (20f) IF an entity X is in Y AND Y is a physical region THEN [presumed] X is physically-in Y.
Thus, the system allows an abstract notion of “in.” That the ideas are physically in the store-rooms is itself a product of the metaphor-based reasoning. (20g) IF X is physically-in R AND R is physically-within S THEN [presumed] X is physically-in S.
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(20h) IF Y is a physical-region AND X is a physical-region AND Y is a part of X THEN [presumed] Y is physically-within X. (20i) IF R1 and R2 are rooms AND R1 and R2 are different THEN [presumed] R1 and R2 are physically-disjoint. (Two regions are physically disjoint of they do not overlap.) (20j) IF O1 is physically-in R1 AND O2 is physically-in R2 AND R1 is physically-within R AND R2 is physically-within R AND R1 and R2 are physically-disjoint THEN [presumed] NOT(O1 and O2 are physically-together with respect to R). This recognizes that physical togetherness is relative to the spatial region concerned (R here). Things in R can be physically together with respect to the physical sizes and distances typical in R, but those same things might not be judged physically together at a smaller scale. (20k) IF O1 is physically-in R AND O2 is physically-in R AND X can-physically-operate-within-region R AND X is a person AND NOT(O1 and O2 are physically together with respect to R) THEN [presumed] NOT(X can-physically-operate-on fO1, O2g). Most of these rules are merely defaults, so the example strongly illustrates claim (9). 3.7.4
Target Domain Rules
An important feature of ATT-Meta is that it has a fully general mechanism for intertwining, in its top space, ordinary reasoning about the the target domain with metaphorical reasoning (i.e. reasoning that proceeds within the pretence cocoon or across the divide between the target and the system’s top-level reasoning 22
space). Any hypothesis in the top space can receive support from or be argued against by any mix of the two types of reasoning. A simple case in the current example is that the system would normally reason that, given that John can mentally operate on two ideas individually, he would normally be able to mentally operate on them conjointly. The metaphorical reasoning to be described will defeat that default. The default arises from the following rule that operates entirely within the target domain (mental states): (21) IF X can-mentally-operate-on J AND X can-mentally-operate-on K THEN [presumed] X can-mentally-operate-on fJ,Kg.
The system will be able to infer that John can mentally operate on I1 and I2 individually by virtue of a piece of metaphorical reasoning using conversion rule (19), from the information that the ideas are in John’s mind. Thus the non-metaphorical, target-domain rule (21) can then operate, thereby conspiring with that metaphorical reasoning to support the target-domain hypothesis that John can mentally operate on the two ideas conjointly. This conflicts with the Primary Connotation that we are trying to establish, also supported by metaphorical reasoning. 3.7.5
Reasoning Goal Supplied by User
We suppose that the user supplies the following goal, i.e, hypothesis for the system to investigate: John can-mentally-operate-on fI1, I2g.
(G)
In fact, the system will conclude with a certainty of presumed for the negation of this, thereby drawing the Primary Connotation. 3.7.6
Target-Domain Facts Supplied by User
The user supplies (at least) the following target-domain facts. I1 is an idea. I2 is an idea. John is a person. We assume that these facts would in reality be determined from the input discourse by some natural-language front-end processing. I1 and I2 are arbitrary constants used by the system for referring to the ideas mentioned in the discourse. The facts have certainly level certain, as is usually the case for facts in our current practice.
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3.7.7
Source-Based Pretence Facts Supplied by User
The user supplies the following facts, which again we suppose would in reality be found by natural-language front-end processing. Again, SR1 and SR2 are arbitrary constants, and the facts are all certain. (22a) It is pretended that: SR1 is a store-room, with certainty certain. (22b) It is pretended that: SR2 is a store-room, with certainty certain. (22c) It is pretended that: SR1 is a part of John’s mind, with certainty certain. (22d) It is pretended that: SR2 is a part of John’s mind, with certainty certain. (22e) It is pretended that: SR1 and SR2 are different, with certainty certain. (22f) It is pretended that: I1 is in SR1, with certainty certain. (22g) It is pretended that: I2 is in SR2, with certainty certain. 3.7.8
Progress of the Reasoning
Roughly, the reasoning depicted in Figure 3 proceeds as follows. (G) is the provided reasoning goal. It matches the THEN-part of target-domain rule (21). As a result, the following subgoal is created: (23a) John can-mentally-operate-on I1. By a process of metaphorical reasoning to be described shortly, this goal achieves level presumed. Then the following subgoal is created and supported in a similar way: (23b) John can-mentally-operate-on I2. Thus (G) gets, tentatively, a certainly level of presumed. Hypothesis (23a) (and similarly (23b)) receives its support via conversion rule (19) for MIND AS PHYSICAL SPACE. The first condition of that rule (with J instantiated to I1) matches a given fact, so the following goal is set up in the top-level space: (24) it is being pretended that, presumably at least, I1 is physically-in John’s-mind. Therefore, the following subgoal is set up within the pretence cocoon: (24a) I1 is physically-in John’s-mind. This achieves certainty level presumed within the cocoon by a straightforward reasoning process from the within-cocoon facts that I1 is in SR1, SR1 is a store-room, and SR1 is a part of John’s mind. These facts arise because of the top-level space facts (22a), (22c) and (22f). The reasoning process uses physical rules (20a) to (20h), except for (20e).
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Because (24a) achieves presumed, (24) itself is deemed to be supported to level presumed. Note that, in the Figure, the action of a conversion rule is depicted by a thick arrow coming from propositions inside the cocoon, but in reality the conversion rule works on the source-based pretence hypotheses outside the cocoon that correspond to the within-cocoon propositions. Eventually the negation of (G), (NOT-G), is also investigated. This matches the THEN-part of conversion rule (17b) for IDEAS AS PHYSICAL OBJECTS, substituting I1 for J, and so on. The IF part causes the following goal, among others, to be set up in the top level space: It is being pretended that, presumably at least, NOT(John can-physically-operate-on fI1, I2g). As a result, the following goal is set up in the cocoon: NOT(John can-physically-operate-on fI1, I2g). This (together with other subgoals not discussed) is supported by straightforward reasoning within the cocoon, based on the within-cocoon facts deriving from the user-supplied facts (22a) to (22g), and using physical rules (20a) to (20k). The following are some of the highlights of this process. For the purposes of applying rule (20k) within the cocoon, the system finds out that, within the cocoon, John can physically operate within John’s mind (not shown in the Figure). This is supported because the corresponding source-based pretence hypothesis is supported via the MIND AS PHYSICAL SPACE refinement rule (19a). This hypothesis is supported because it is established within the cocoon that John’s-mind is a physical region (not shown in the Figure). Application of physical rule (20k) also requires reasoning about the within-cocoon goal (29) John is a person (30) NOT(I1 and I2 are physically together with respect to John’s-mind) Hypothesis (29) is supported by a process to be discussed in Section 3.7.10. Hypothesis (30) matches the conclusion part of physical rule (20j). The system eventually finds that with SR1 and SR2 as values for variables R1 and R2 in (20j), all the conditions can be supported to level presumed. Altogether we have now seen in outline how some evidence is found that leads to a presumed level for (G), and other evidence leads to a presumed level for (NOT-G). 3.7.9
Conflict Resolution in Store-Room Example
The specificity-based mechanism is adequate for establishing that the evidence supporting goal (NOT-G) above it be more specific than the evidence supporting (G) itself. The argumentation for (G) ultimately relies on the target-domain facts and the source-based pretence facts (22a) to (22d), (22f) and (22g). It does
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not rely on (22e). However, the argument for (NOT-G) relies on all seven of those facts (and the targetdomain facts). Therefore, merely by the fact-based aspect of specificity comparison, (NOT-G) is regarded as having more specific support. (G) and (NOT-G) are both supported by conversion rules (for different metaphorical views). However, (G) could be supported by information that is entirely derived in the target domain. For instance, the understander might know a fact F from previous non-metaphorical discourse that allows it to be concluded that John is entertaining I1 and I2, and therefore can mentally operate on I1 and I2 individually, so that by target-domain rule (21) we get further support for (G). However, although we do not show here how this happens, (NOT-G) would still win against (G). This is an example of source-over-target overriding, licensed by (10). 3.7.10
Fact Importation
Recall that one goal set up in the cocoon was (29) John is a person This resulted from considering rule (20k). Although it is established that John is a person outside the cocoon, it needs to be established inside, for the purposes of (20k). In our approach, a source-based pretence can use information about reality insofar as it does not lead to the defeat of hypotheses derived from specific pretence premises such as those in section 3.7.7. Accordingly, ATT-Meta imports facts from outside to inside, subject to two qualifications. First, if the fact is certain, it is demoted to presumed. Secondly, the specificitycomparison mechanism gives preference to those aspects of arguments that do not rely on imported facts. More precisely, specificity comparison is first tried with all evidence directly or directly relying on imported facts ignored. If this does lead to a winner being established, the ignored rules applications are brought back in and specificity comparison tried again. A simple case is when one of the two hypotheses relies entirely on imported facts but the other does not. Then the latter wins. The reason for the qualifications is that imported facts, or conclusions that can be drawn from them within the cocoon, can conflict with hypotheses resulting from pretence. For instance, ATT-Meta has a rule not mentioned above: IF X is a mind THEN [certain] NOT(X is a physical-region) Suppose we give ATT-Meta the obvious certain fact that John’s-mind is a mind. Then this fact is imported (with level presumed), and within the cocoon there is an argument for NOT(John’smind is a physical space). This conflicts with the within-cocoon hypothesis that John’s mind is a physical region (because it has rooms). The qualifications ensure that this latter hypothesis wins the conflict.
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3.8 Need for the Connotation The store-room example as presented so far is does not address the question of why understanders would try to establish the Primary Connotation addressed in section 3.7, or how understanders would make use of the connotation if they spontaneously derived it. But suppose that the two ideas (I1, I2) mentioned in the store-rooms sentence (18) are stated (elsewhere in the discourse) to be believed by the agent, John, and suppose that they lead by some fairly direct, natural reasoning to some conclusion C. Then, if (18) had not been uttered, it would have been reasonable to ascribe by default to John a belief in the conclusion C. But this ascription relies on a default assumption that John does mentally operate conjointly on I1 and I2. If there is evidence that this is not the case, then the ascription should be abandoned. Sentence (18) provides just such evidence against John’s mentally operating conjointly on I1 and I2. ATT-Meta is designed to perform reasoning about beliefs in general—i.e., without as well as with metaphorical input—and can cope with this extension of the example. We do not detail the processing here, but a similar example is treated in Barnden (1998a). The key to the treatment is that in trying to ascribe the argument to C from I1 and I2 to John, ATT-Meta sets up the hypothesis that John mentally operates conjointly on I1 and I2 as a goal. There is a special default rule that gives this hypothesis an initial level of presumed, but the metaphorical processing defeats it.
4 Further Discussion Having presented our general claims and approach and outlined the computer implementation, we now make some further arguments, link the description of ATT-Meta more explicitly to some of our general claims, clarify the scope of the approach, and compare and contrast the approach to a partially similar proposal in Philosophy.
4.1 Power of Mappings We have only discussed a few connotations of a few example sentences, resting on a small number of illustrative mapping rules. However, we believe that a few such mapping rules can handle a large, openended array of other manifestations of the metaphorical views concerned. Let us concentrate on conversion rules (17a) and (17b) for IDEAS AS PHYSICAL OBJECTS and (19) for MIND AS PHYSICAL SPACE. These, together perhaps with a small number of others, and together with the mappings implied by claim (13), account for examples involving ideas being in “recesses” of a mind, being “buried” in a mind, being “far apart” in a mind, etc. In fact, anything that implies some physical inaccessibility of ideas also implies by default some difficulty in physically operating on them, and therefore (via a conversion rule related to 17b and a difficulty conversion rule sanctioned by claim 13) difficulty in mentally operating on them. Anything that implies considerable physical apartness of two ideas also implies by default that they cannot be operated on conjointly. Further, anything that implies physical movement of an idea so that it comes to be, or ceases
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to be, in “recesses” “buried,” etc., or so that it comes to be or ceases to be physically considerably separated from another idea can be handled, because coming to be or ceasing to be in a state is mapped over according to claim (13). If ideas “whiz about” in a mind then they are difficult to “grab,” so that they are difficult to physically operate upon, and hence, once again, difficult to mentally operate on. On the other hand, they are visually salient in the mind-space, so that the agent is strongly mentally aware of them; here we have appealed to a seeing-to-awareness mapping forming part of a COGNIZING AS PERCEIVING metaphorical view. If an idea “comes to someone [X] out of nowhere” then, prior to that event, X could not “see” the idea and was hence unaware of it. Even if we take a very colorful example such as where an idea “sticks to someone [X] with burr-like tenacity” (from an example observed in a detective novel) we can still readily account for it: a physical object that is sticking to a person is by default perceptually salient to the person, so that in the example the idea is, by conversion under COGNIZING AS PERCEIVING, in the person’s awareness. The burr-like tenacity serves to emphasize the longevity of the sticking and resistance to attempts to end it. These are matters that can be handled by the type of mapping envisaged in claim (13). Such considerations make it plausible that a small number of conversion rules per metaphorical view such as IDEAS AS PHYSICAL OBJECTS, MIND AS PHYSICAL SPACE and COGNIZING AS PERCEIVING, plus mappings sanctioned by claim (13), can allow very many, if not most, manifestations of such views to be handled by our approach.
4.2 No Identification of the Metaphorical View Our examples illustrate the fact that our approach does not require the understander to identify what metaphorical view is involved. This is because the individual conversion rules themselves contain conditions explicitly or implicitly ensuring appropriate application. For example, conversion rule (19) contains a condition requiring that idea J be physically in person X’s mind. The rule is therefore applicable only when X’s mind is a physical region or occupies a physical region. Similarly, conversion rule (17a) has a condition about physically operating on ideas J and K. This implicitly requires J and K to be physical objects (within the pretence). Further, such conditions can become satisfied at any time during metaphorical reasoning, not just at the beginning. The fact that it is merely a mid-reasoning inference that the ideas in the shoving and storeroom examples (15,18) are physical objects (in the pretence cocoon ) is one reason why we reason about the ideas themselves in the cocoon, rather than about concocted physical objects that are merely analogical counterparts to the ideas. Before the inference has been made it is not clear that the ideas are being viewed metaphorically at all.
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4.3 Detection of Metaphoricity Our approach assumes that the understander builds a metaphorical pretence cocoon. Therefore, the understander does need to detect that the utterance is metaphorical, or, more precisely, decide that the utterance should be taken metaphorically. With several other authors we would suggest that in many cases metaphoricity is raised as a strong possibility because of the violation of selection restrictions (Fass, 1997; Wilks, 1978), for example the restriction that “gobble” expects an animal agent. This approach could include noticing that “store-rooms in his mind” violates a restriction of a meaning of “in”, for instance a restriction that the two things are usually either both physical or both abstract. Similarly for “ideas ... in ... store-rooms.” However, selection restrictions are just reasoning rules in a particular form, and so the processing of them prior to setting up the pretence cocoon is still a matter of the cocoon arising in the middle of reasoning. In some cases more elaborate reasoning will need to be done before metaphoricity is detected, because, as is often pointed out in the literature, utterances can have both a metaphorical and a purely literal interpretation, and both interpretations can depend on the same literal meanings of the words. An example is “John climbed to the top.” This has a literal interpretation in the domain of ordinary physical climbing, and a metaphorical interpretation in, say, the career domain. In such cases, we envisage that the understander starts to process the utterance on the assumption that it is literal, performing inferences on the way, finds an indication at some point, early or late, that the utterance is metaphorical, and, if that indication is strong enough, constructs a pretence cocoon. The understander then moves some or all of the reasoning that has already been done into the pretence cocoon, and then performs more reasoning inside and outside the cocoon. We have not yet implemented this mechanism.
4.4 Direction and Metaphor-Neutrality of Overriding In the Store-Rooms example, we pointed out that the metaphorical support for (NOT-G) can defeat the nonmetaphorical, target-domain support for (G) (as well as the metaphor-based support for (G)). This illustrates claim (10) above. The following sentence provides another example: (31) “The company nursed its competitor back to health” This would presumably be intended to defeat the usual presumption that companies do not help each other. Many examples of metaphor that we have seen in discourse are like this in serving to defeat target-domain defaults that would otherwise hold sway. It is true, for instance, of many of the mental-metaphor examples in our databank (Barnden, n.d.). Indeed, there is a prima facie case for saying that exceptional situations are more likely to fall outside the normal provision of the language, making it more likely that a metaphorical utterance with non-map-enshrined notions will be needed. The opposite sort overriding—where non-metaphor-based target information overrides attempted metaphorical transfers—is the type of overriding that is almost always focused on, and it is certainly important. The
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simplest case, and the one that seems to be the only one considered in most other researchers’ work, is where the understander is certain that some target proposition T is true, where T conflicts with some attempted transfer. Obviously T should win in this case, and ATT-Meta handles it easily. What is more interesting is when the transfer conflicts with target proposition T not believed with complete certainty by the understander. ATT-Meta would resolve such a conflict by means of the conflict resolution mechanism outlined in section 3.6. That mechanism is metaphor-neutral in that it does not distinguish between metaphor-based and nonmetaphor-based arguments. This is adequate for dealing both with the conflict between (G) and (NOT-G) in the store-rooms example and example (31). The fact that the transferred hypothesis H that the company helped its competitor defeats a target-domain default is nothing to do with H being a metaphorical transfer. It is just that it is something that relies on the specific information supplied by the utterer of (31). The targetdomain defaults would be equally well overridden by non-metaphorical but still uncertainty-laden sentences like “It seems the company helped its competitor become viable again.”
4.5 Allowing Transfer of Non-Map-Enshrined Items So far it may seem that we claim that the understander should never try to transfer non-map-enshrined aspects in manifestations of metaphors to the target domain. In actuality, our general claim is only that we should not assume that an understander must try to transfer non-map-enshrined elements of the source domain that are referred to in the utterance. But there may be occasions when the understander could work out a mapping for the non-map-enshrined item quite easily, and may in fact work it out spontaneously with no particular goal in mind, or where the understander may be pressured to find a mapping for the non-mapenshrined item. As an example of the last point, suppose someone were to say “Jack attacked the plumbing of Sally’s theory,” a sentence that manifests the THEORY AS BUILDING metaphorical view (see, e.g., Grady, 1997, for discussion of this view). Then, clearly, in order to understand what it is that John attacked, the understander is pressured to work out a mapping for the plumbing (which we assume is a non-mapenshrined item). This paper does not address the issue of how non-map-enshrined source items can be transferred, but the the map-discovery and transfer methods used in systems such as SME could presumably be used.
4.6 Immediately Mappable and Stock Manifestations We have emphasized the important role of within-cocoon, source-based reasoning to link the metaphorical utterance’s source-based (“literal”) meaning to hypotheses that the known mappings can work on. But, of course, we allow that, as a special case, some or all of the hypotheses constituting the source-based meaning can be immediately mapped. Note carefully, however, that it is still possible for the connotations that are actually of interest in the discourse to rely on source hypotheses that must be derived by source-based inference from the source-based meaning of the utterance.
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It may be that some words or phrase for some map-enshrined notions are listed in the understander’s lexicon, together with their target-domain meanings. Presumably, this is especially the case for fixed idioms and “stock” words/phrases that manifest a metaphorical view (and for locutions that are only historically metaphorical—“dead metaphors”). For example, the phrase-template “in the back of [one’s] mind” occurs so frequently in real discourse it might make sense to list it as meaning something like “in a subsidiary way.” We certainly allow this, and our reasoning approach is not needed in such cases. However, it is well known that discourse often uses variants of idioms and stock phrases (Moon, 1998). For coping with such variants (e.g., “further towards the back of [one’s] mind”) we claim that one often needs to return to explicitly metaphorical processing, such as ours. This is in line with claims of Cacciari & Levorato (1998) and Kittay (1989). An aspect of this question of variants is illustrated by the store-rooms example, (18). Suppose the notion of store-rooms were map-enshrined for an understander as part of knowledge of MIND AS PHYSICAL SPACE, or even that “store-room” were a stock phrase applied to the mind. Our source-based reasoning approach is still beneficial for handling (18), because it is the distinctness of the two store-rooms that is crucial for the discussed connotation. And the distinctness only leads to the desired connotation because two distinct physical rooms generally do not have any spatial overlap. Without the source-based reasoning approach, the target domain would need to contain knowledge and inference patterns to support reasoning parallel to the source-based reasoning we have suggested. For instance, distinctness of mental entities that correspond to store-rooms would have to have just the right inferential effects. This approach is both theoretically and operationally less economical than our source-based approach.
4.7 Relationship to Davidson’s Approach Because our approach tends to avoid trying to map non-map-enshrined notions in a metaphorical utterance to the target domain, it tends towards not computing a complete metaphorical meaning of the utterance— “complete” in the sense of translating all metaphorically used terms in the sentence into target-domain notions. Instead, the result of understanding the utterance in context is merely some set of connotations that are useful for building an overall picture of what the discourse is conveying. The approach is therefore reminiscent of that of Davidson (1979), who claims that metaphorical utterances have no meanings beyond their literal ones (i.e., source-based ones, in our terminology), as does Cooper (1986). Davidson emphasizes “connotations” as we do. However, our view is less extreme, in that we do allow the possibility that some metaphorical utterances might have metaphorical meanings. At a theoretical extreme, one could always take the set of connotations actually produced in a given understanding episode to be the meaning-in-context of the utterance. Also, we reject other claims of Davidson, such as that connotations are non-propositional in nature.
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4.8 Goal-Directedness and Relevance Notice that any metaphorical utterance will generally only be made where at least one of its connotations is relevant to the surrounding context. For example, a sentence such as “The two ideas were in different store-rooms of John’s mind” will be in some context where it matters that the ideas in question are in different store-rooms, as discussed in section 3.8. If understanders are predisposed to seek coherence relationships between utterances (cf. Hobbs, 1985, and Mann & Thompson, 1987), then the goal to establish a relationship will lead back to the suggestion to investigate particular, apparently relevant connotations of metaphorical utterances. The reasoning will in turn proceed backwards from these connotations. Backwards, goal-directed reasoning of this sort is what our approach advocates in the main (although it is possible that a certain amount of inference should proceed in a forwards direction from the source-based meaning in the cocoon). It answers, partially at least, the question of how an understander is to focus on any particular one of the many connotations that a metaphorical utterance could have. Another benefit of the goal-directed reasoning is that it gives a partial answer to the notorious problem of how to confine metaphorical processing to only those aspects of the source domain that are relevant in a particular manifestation of a metaphorical view. Only those analogical mappings that are relevant to some unresolved discourse “problem” are used, and only the source information inferentially connected to the source sides of those analogical mappings is used. (Some other work on analogy and metaphor has allowed for some degree of goal-sensitivity, including ACME, the SME version mentioned by Markman (1997), and IAM.)
5 Conclusion We have provided a detailed approach to the handling of exploitative metaphorical utterances. Although analogy-based, it contrasts with mainstream analogy-based approaches in deemphasizing the creation of mappings in favor of an emphasis on on-the-fly source-based inference, and in not requiring transferred information to be based on information that is itself mapped (claims (7) and (8) in section 2). The approach helps to plug an important gap in current research on metaphorical processing, though we do not claim that it copes with all cases of exploitation of familiar metaphors, since some cases may require the construction of new mappings. The existence of the ATT-Meta system provides evidence that the general approach can be put into operation. It also helps to clarify the nature of the claims (in Section 2) on which the approach is based. Although a small amount of other computationally-detailed work on metaphor (mainly Hobbs, 1990, and Narayanan, 1997) implicitly shares one or two of our claims, notably on the importance of source-based reasoning, that other work does not argue for our other claims. There is nothing in our approach that would prevent combination with an account of how to deal with manifestations of unfamiliar metaphorical views, for instance an account based on SME’s approach. The processing could discover mappings from scratch and express in the form of our conversion rules. Our
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advocated style of processing would then be available. Naturally, we also allow that conversion rules could be learned during development of the person or AI system. A distinctive feature of the approach is the emphasis on thoroughly and smoothly embedding the handling of metaphor in a general-purpose uncertain-reasoning framework. Hobbs (1990) also does this, but we make claims about uncertainty that he has not emphasized—although we suspect that they would congenial to him—and that are largely novel with respect to other metaphor research as well. Notably, we claim that the mappings involved in a metaphorical view can be individually uncertain and can conflict with each other. We claim that metaphorical inferences about the target domain should often override target-domain defaults. We claim that source/target conflicts should be resolved by general conflict-resolution mechanisms, not by metaphor-specific principles. We even allow the understander to be uncertain as to what the nature of the metaphorical view is. For instance, in the treatment of the store-rooms example (18) it is merely a derived working assumption— in the form of two inferred presumed hypotheses not discussed in section 3—that the ideas mentioned in the sentence are being regarded as physical objects. These hypotheses were open to defeat just as any non-certain hypothesis is, although in the example they are not defeated.
6 Acknowledgment The research was supported in part by grant number IRI-9101354 from the National Science Foundation (USA).
7 References Barnden, J.A. (1997). Consciousness and common-sense metaphors of mind. In S. O’Nuallain, P. McKevitt & E. Mac Aogain (Eds), Two Sciences of Mind: Readings in Cognitive Science And Consciousness, pp.311–340. Amsterdam/Philadelphia: John Benjamins. Amsterdam/Philadelphia: John Benjamins. Barnden, J.A. (1998a). An AI system for metaphorical reasoning about mental states in discourse. In Koenig, J-P. (Ed.), Discourse and Cognition: Bridging the Gap. Stanford, CA: CSLI. Barnden, J.A. (1998b). Combining uncertain belief reasoning and uncertain metaphor-based reasoning. In Procs. Twentieth Annual Meeting of the Cognitive Science Society, pp.114–119. Mahwah, N.J.: Lawrence Erlbaum Associates. Barnden, J.A. (1998c). Uncertain reasoning about agents’ beliefs and reasoning. Technical Report CSRP98-11, School of Computer Science, The University of Birmingham, U.K. Also in Artificial Intelligence and Law, in press. Barnden, J.A. (n.d.). ATT-Meta Project Databank. http://www.cs.bham.ac.uk/˜jab/ATT-Meta/Databank. 33
Barnden, J.A., Helmreich, S., Iverson, E. & Stein, G.C. (1994a). An integrated implementation of simulative, uncertain and metaphorical reasoning about mental states. In J. Doyle, E. Sandewall & P. Torasso (Eds), Principles of Knowledge Representation and Reasoning: Proceedings of the Fourth International Conference, pp.27–38. San Mateo, CA: Morgan Kaufmann. Barnden, J.A., Helmreich, S., Iverson, E. & Stein, G.C. (1994b). Combining simulative and metaphor-based reasoning about beliefs. In Procs. 16th Annual Conference of the Cognitive Science Society (Atlanta, Georgia, August 1994), pp.21–26. Hillsdale, N.J.: Lawrence Erlbaum. Barnden, J.A., Helmreich, S., Iverson, E. & Stein, G.C. (1996). Artificial intelligence and metaphors of mind: within-vehicle reasoning and its benefits. Metaphor and Symbolic Activity, 11(2), pp.101–123. Black, M. (1979/1993). More about metaphor. In A. Ortony (Ed.), Metaphor and Thought, 2nd edition, pp.19–41. New York and Cambridge, U.K.: Cambridge University Press. Reprinted from first edition, 1979. Boers, F. (1997). “No pain, no gain” in free market rhetoric: a test for cognitive semantics? Metaphor and Symbol, 12(4), pp.231–241. Cacciari, C. & Levorato, M.C. (1998). The effect of semantic analyzability of idioms in metalinguistic tasks. Metaphor and Symbol, 13(3), pp.159–177. Carbonell, J.G. (1982). Metaphor: an inescapable phenomenon in natural-language comprehension. In W. Lehnert & M. Ringle (eds), Strategies for Natural Language Processing, pp.415–434. Hillsdale, N.J.: Lawrence Erlbaum. Clausner, T.C. & Croft, W. (1997). Productivity and schematicity in metaphors. Cognitive Science, 21(3), pp.247–282. Cooper, D.E. (1986). Metaphor. Oxford, U.K.: Blackwell. Croft, W. (1998). Linguistic evidence and mental representations. Cognitive Linguistics, 9(2), pp.151–173. Davidson, D. (1979). What metaphors mean. In S. Sacks (Ed.), On Metaphor. University of Chicago Press. Deignan, A. (1999). Linguistic metaphors and collocation in nonliterary corpus data. Metaphor and Symbol, 14(1), pp.19–36. Falkenhainer, B., Forbus, K.D. & Gentner, D. (1989). The Structure-Mapping Engine: algorithm and examples. Artificial Intelligence, 41 (1), 1–63. Fass, D. (1997). Processing metaphor and metonymy. Greenwich, Connecticut: Ablex. Fauconnier, G. & Turner, M. (1998). Conceptual integration networks. Cognitive Science, 22(2), pp.133– 187. Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7 (2), 95–119.
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Gerrig, R.J. (1989). Empirical constraints on computational theories of metaphor: comments on Indurkhya. Cognitive Science, 13 (2), pp.235–241. Gibbs, R.W., Jr. (1989). Understanding and literal meaning. Cognitive Science, 13, pp.243–251. Gibbs, R.W., Jr. (1998). The fight over metaphor in thought and language. In A.N. Katz, C. Cacciari, R.W. Gibbs, Jr., and M. Turner, Figurative Language and Thought, pp.88–118. New York, Oxford: Oxford University Press. Gibbs, R.W., Jr. (1990). Comprehending figurative referential descriptions. J. Experimental Psychology: Learning, Memory and Cognition, 16 (1),pp.56–66. Gibbs, R.W., Jr., Johnson, M.D. &, Colston, H.L. (1996). How to study proverb understanding. Metaphor and Symbolic Activity, 11(3), pp.233–239. Gineste, M-D. & Scart-Lhomme, V. (1998). Comment comprenons-nous les m´etaphores? L’Ann´ee Psychologique, 1998. Giora, R. (1997). Understanding figurative and literal language: The graded salience hypothesis. Cognitive Linguistics, 8(3), pp.183–206. Glucksberg, S. & Keysar, B. (1990). Understanding metaphorical comparisons: beyond similarity. Psychological Review, 97 (1), pp.3–18. Grady, J.E. (1997). THEORIES ARE BUILDINGS revisited. Cognitive Linguistics, 8(4), pp.267–290. Hobbs, J.R. (1985). On the coherence and structure of discourse. Report No. CSLI–85–37, Center for the Study of Language and Information, Stanford University, Calif., USA. Hobbs, J.R. (1990). Literature and cognition. CSLI Lecture Notes, No. 21, Center for the Study of Language and Information, Stanford University. Holyoak, K.J., Novick, L.R. & Melz, E.R. (1994). Component processes in analogical transfer: mapping, pattern completion, and adaptation. In K.J. Holyoak & J.A. Barnden (Eds), Advances in Connectionist and Neural Computation Theory, Vol. 2. Norwood, N.J.: Ablex Publishing Corp. Holyoak, K.J. & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13 (3), 295–355. Honeck, R.P., Welge, J. & Temple, J.G. (1998). The symmetry control in tests of the standard pragmatic model: The case of proverb comprehension. Metaphor and Symbol, 13(4), pp.257–273. Iverson, E. & Helmreich, S. (1992). Metallel: an integrated approach to non-literal phrase interpretation. Computational Intelligence, 8 (3), pp.477–493. Keane, M. (1988). Incremental analogising: Theory and model. HCRL Tech. Rept. No. 38, Human Cognition Research Laboratory, Milton Keynes, U.K. Also in K. Gilhooly, M. Keane, R. Logie & G. Erdos (Eds), Lines of Thought: Reflections on the Psychology of Thinking, Chichester, U.K.: Wiley.
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Kittay, E.F. (1989). Metaphor: its cognitive force and linguistic structure. (Paperback ed.) Oxford, U.K.: Clarendon Press. Kolodner, J.L. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann. Lakoff, G. (1993). The contemporary theory of metaphor. In A. Ortony (Ed.), Metaphor and Thought, 2nd edition. Cambridge University Press. Lakoff, G. & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press. wLakoff, G. & Turner, M. (1989). More than cool reason: a field guide to poetic metaphor. U. Chicago Press. Levin, S.R. (1993). Language, concepts, and worlds: three domains of metaphor. In A. Ortony (Ed.), Metaphor and Thought, 2nd edition, pp.112–123. New York and Cambridge, U.K.: Cambridge University Press. Loui, R.P. (1987). Defeat among arguments: a system of defeasible inference. Computational Intelligence, 3, pp.100–106. Lytinen, S.L., Burridge, R.R. & Kirtner, J.D. (1992). The role of literal meaning in the comprehension of non-literal constructions. Computational Intelligence, 8 (3), pp.416–432. Mann, W.C. & Thompson, S.A. (1987). Rhetorical theory of text organization. Text, 8 (3), pp.167–182. Markman, A.B. (1997). Constraints on analogical inference. Cognitive Science, 21(4), pp.373–418. Martin, J.H. (1990). A computational model of metaphor interpretation. Academic Press. Martin, J. (1994). Metabank: A knowledge-base of metaphoric language conventions. Computational Intelligence, 10 (2), pp.134–149. Moon, R. (1998). Fixed idioms and expressions in English. Clarendon Press: Oxofrd, U.K. Narayanan, S. (1997). KARMA: Knowledge-based action representations for metaphor and aspect. Ph.D. thesis, Computer Science Division, EECS Department, University of California, Berkeley, August 1997. Onishi, K.H. & Murphy, G.L. (1993). Metaphoric reference: when metaphors are not understood as easily as literal expressions. Memory and Cognition, 21 (6), pp.763–772. Ortony, A. (1979). The role of similarity in similes and metaphors. In A. Ortony (Ed.), Metaphor and Thought, pp.186–201. Cambridge, U.K.: Cambridge University Press. R´ecanati, F. (1993). The alleged priority of literal interpretation. Cognitive Science, 19(2), pp.207–232. Reddy, M.J. (1979/1993). The conduit metaphor—a case of frame conflict in our language about language. In A. Ortony (Ed.), Metaphor and Thought, Cambridge, UK: Cambridge University Press. Reprinted in second edition, 1993, pp.164–201. Sun, R. (1995). A microfeature based approach towards metaphor interpretation. In Procs. Fourteenth Int. Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, Canada, pp.424-429 36
Veale, T. & Keane, M.T. (1992). Conceptual scaffolding: a spatially founded meaning representation for metaphor comprehension. Computational Intelligence, 8 (3), pp.494–519. Veale, T. & Keane, M.T. (1997). The competence of sub-optimal structure mapping on ‘hard’ analogies. In Procs. Int. Joint Conf. On Artificial Intelligence (Nagoya, Japan), August 1997. Wilks, Y. (1978). Making preferences more active. Artificial Intelligence, 10, pp.75–97.
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FIGURE CAPTIONS Figure 1 A schematic depiction of the reasoning involved in the Gobbling Company example (Section 2.4). The source-based meaning of the sentence is shown in the topmost part of the large box. Statements not in quotation marks stand for expressions in some representation scheme used internally by the understander. Arrows between such statements show inferential links. The processing shown within the large box is source-based inference. The processing outisde the box is reasoning within the terms of the target domain. The arrows from within the box to outside depict applications of source-to-target mapping rules. Since these applications create propositions that are in terms of the target domain, they count as steps of “transfer” in the sense commonly used in the field of analogy.
Figure 2 Showing the reasoning involved in the Shoving example (Section 2.5).
Figure 3 Showing part of the reasoning involved in the Store-Rooms example (Section 3.7). The arrow from within the cocoon to outside depicts two applications of conversion rule (19) working on the (source-based pretence hypotheses corresponding to) two within-cocoon hypotheses. (The source-based inference supporting these propositions is not shown.) The lower arrow from within the cocoon to outside depicts an application of conversion rule (17b). That application also uses the facts that I1 and I2 are ideas and that John is a person, but these uses are not shown. Also not shown are the source-based pretence hypotheses, and the importation of the fact that John is a person into the cocoon (see Section 3.7.10).
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‘‘The company gobbled up its competitor’’ the company = C, competitor = D SOURCE-BASED MEANING:
C is a company
C gobbled-up D
C is an organization
C is an animal C is phsyical object SC is the substance of C SD is the substance of D SC is the resource set of C SD is the resource set of D
C ate D up rapidly
C caused rapid change from SD not physically-within SC to SD physically-within SC
C caused rapid change from SD not included-within SC to SD included-within SC
C acquired D rapidly SOURCE-BASED PRETENCE COCOON
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‘‘John shoved the two ideas together" The ideas are I1 and I2 SOURCE-BASED MEANING: John shoved-together I1 and I2 John physically-moved-together I1 and I2 John physically-operated on I1 John physically-operated on I2 John can-physically-operate on I1 John can-physicall-operate on I2
I1 and I2 are physically-together
John can-physically-operate on I1 and I2 conjointly John can-mentally-operate on I1 and I2 conjointly
SOURCE-BASED PRETENCE COCOON
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‘‘The ideas are in different store-rooms in John’s mind" The ideas are I1 and I2 SOURCE-BASED MEANING: SR1 and SR2 are store-rooms SR1 and SR2 are parts of John’s mind SR1 and SR2 are different I1 and I2 are in SR1 and SR2 resp.
NOT(SR1 and SR2 are physically-together with respect to John’s mind) I1 is physically-in John’s mind I2 is physically-in John’s mind
John can mentally-operate on I1 John can mentally-operate on I2
NOT(I1 and I2 are physically-together with respect to John’s mind) John can-mentally-operate on I1 and I2 conjointly defeated
goal G
NOT(John can-physically-operate on I1 and I2 conjointly) NOT(John can-mentally-operate on I1 and I2 conjointly) NOT-G SOURCE-BASED PRETENCE COCOON
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