Computational Metaphor Identification: a Method for Identifying Conceptual Metaphors in Written Text

COMPUTATIONAL METAPHOR IDENTIFICATION: A METHOD FOR IDENTIFYING CONCEPTUAL METAPHORS IN WRITTEN TEXT Eric Baumer1 [email protected] Bill Tomlinson1 [email protected] Lindsey Richland2 [email protected] 1 Department of Informatics, Donald Bren School of Information and Computer Sciences, 5029 Bren Hall 2 Department of Education, Berkeley Place 2046 University of California, Irvine Irvine, CA 92697 ABSTRACT While much research has pursued the de- velopment of computational models of metaphorical and analogical reasoning erate as output analogical correspondences in controlled contexts, less work has been con- mapping attributes of the source onto the target ducted on model capturing less structured, (though see Doumas, Hummel, & Sandhofer, everyday relational reasoning. This paper de- 2008). However, as noted previously (Falken- scribes computational metaphor identification hainer et al., 1989), such an approach requires (CMI), which uses computational linguistic encoding knowledge into a representation techniques to identify patterns in written text amenable to such processing. It may be bene- indicative of potential conceptual metaphors. ficial to explore alternatives based on deriving The paper presents an overview of CMI, fol- such mappings by analyzing the form in which lowed by sample results from two different many human ideas are conveyed: written corpora: middle school students' science writ- natural-language. ing and political blogs from during the 2008 In a separate body of research, computa- US election. These results demonstrate CMI's tional linguists have grappled with issues re- capacity to identify linguistic patterns poten- lated to metaphor and analogy, e.g., (Fass, tially indicative of deep conceptual metaphors 1991; Lu & Feldman, 2007; Martin, 1990), that could subtly yet powerfully influence rea- specifically as related to text, but from a soning. slightly different perspective. Rather than attempting to determine what analogical map- Introduction pings or processes might be at work, these techniques have been designed for natural lan- In order to understand the processes of guage processing (NLP) systems, such as ex- human analogical reasoning, researchers have tracting and codifying knowledge from large created numerous models and processual ac- bodies of text. In this context, the goal is often counts thereof, e.g., (Gentner, 1983; Holyoak to determine whether a given phrase is literal & Thagard, 1989), some of which have been or figurative, and then to perform special addi- implemented in computational systems, e.g., tional processing on figurative text to deter- (Falkenhainer, Forbus, & Gentner, 1989; Hall, mine its literal meaning. Such systems gener- 1989; Hummel & Holyoak, 2003). Most such ally approach metaphorical language on a models take as input encoded knowledge about case-by-case basis, determining if each indi- two or more distinct representations, and gen- vidual phrase they encounter is literal or figu- 20 Computational Metaphor Identification rative, rather than searching for overarching RELATED WORK patterns that may indicate underlying conceptual mappings. Relational reasoning is an integral part of This paper represents a departure from humans' ability to interact generatively and previous approaches to metaphorical language creatively in the world, and enables reasoners and relational reasoning. Specifically, we de- to draw inferences from one domain, or repre- scribe computational metaphor identification sentation, to better conceptualize another (CMI), a technique for identifying linguistic (Penn, Holyoak, & Povinelli, 2008). The work patterns in large textual corpora potentially described here draws on previous linguistics indicative of conceptual metaphors. This ap- work on conceptual metaphor and computa- proach differs from previous computational tional linguistics approaches to develop a new linguistic work in that rather than attempting to system for analyzing cross-domain relational discern whether individual phrases are meta- reasoning in everyday thought. This section phorical or literal, it focuses on overall linguis- briefly reviews the theory of conceptual meta- tic patterns that suffuse a body of text. It also phor and describes a prior attempt to study differs from most previous approaches to mod- them computationally as a foundation to the eling relational reasoning in that, rather than current system. using abstract knowledge representations con- structed by a researcher, this approach focuses Conceptual Metaphor solely on mapping correspondences between linguistic patterns within different corpora of The work presented in this paper is in- written natural language. formed largely by the Lakoffian perspective of This work also represents an important conceptual metaphor (Lakoff, 1993; Lakoff & new direction for AI research. The goal of Johnson, 1980) and views metaphor not as a CMI is not to state definitively the metaphors literary or poetic device, but rather as a fun- that are present in a corpus, but rather to iden- damental aspect of human cognition. For ex- tify for consideration potential metaphors, en- ample, when discussing money, one might say couraging critical reflection on what identified “he poured money into his savings account,” linguistic patterns might imply. This approach “they froze my assets,” or “capital freely is something of an inversion of more tradi- flowed between investors.” Lakoff and John- tional artificial intelligence research. Classical son (1980) claim that such linguistic patterns AI is often associated with asking, “Can peo- evidence the conceptual metaphor MONEY IS A 1 ple make computers think?” That is, can we LIQUID , that we understand the abstract con- develop computational systems that behave in cept of money in terms of our concrete physi- a manner we might call intelligent. Instead, the cal experiences with liquids. We use words work described here asks, “Can computers from our knowledge of liquids to talk about make people think?” That is, can we design money because the cognitive structure of the and implement computational technologies metaphor “sanctions the use of source domain that encourage human users to think in new, language and inference patterns for the target different ways or to approach familiar con- domain” (Lakoff, 1993, p. 208). This is not to cepts and ideas from novel, alternative per- say that conceptual metaphor is a primarily a spectives. Thus, from this perspective, CMI linguistic phenomenon. Rather, the linguistic should be evaluated not on its accuracy at patterns serve as evidence for the cognitive identifying cross-domain mappings, but rather phenomenon. on its ability to find patterns of language that can promote critical thinking and creativity. 1 This paper uses SMALL CAPS for metaphors, italics for concepts, ALL CAPS for domains, and “quotes” for example phrases. 21 Eric Baumer, Bill Tomlinson, and Lindsey Richland One important aspect of conceptual searches about the LABORATORY domain metaphor theory is that many different meta- and the FINANCE domain, based on the way phors may be used to frame the same concept, that verbs such as “pour,” “flow,” “freeze,” a phenomenon referred to as metaphorical plu- and “evaporate” are associated with words for ralism. For example, a cornucopia of meta- liquid in the LABORATORY corpus and with phors can be used for the concept of love, such words for money in the FINANCE corpus. The as LOVE IS A JOURNEY: “this relationship is[n't] technique presented here draws largely on going anywhere;” LOVE IS MADNESS: “I'm just CorMet but differs in two important ways. wild about Harry;” or LOVE IS MAGIC: “she is First, CorMet was designed to extract known bewitching” (Lakoff & Johnson, 1980, pp. conventional metaphors, whereas this work 44,49). Each metaphor simultaneously high- involves identifying potential metaphors in lights certain aspects of a situation while arbitrary corpora. Second, little work has ex- downplaying others. Lakoff and Johnson argue plored using such computationally identified that “successful functioning in our daily lives metaphors to promote critical thinking about, seems to require a constant shifting of [many] and creative generation of, metaphors. metaphors ... that are inconsistent with one another ... to comprehend details of our daily COMPUTATIONAL METAPHOR existence” (Lakoff & Johnson, 1980, p. 221). IDENTIFICATION Moreover, suggestion of an alternative, novel metaphor can provide a reconceptualization While space limits preclude a fully de- that draws attention to different aspects of the tailed description, this section provides an situation, can “cause us to try to understand overview of computational metaphor identifi- how [the novel metaphor] could be true, [and] cation (CMI), a technique for identifying lin- makes possible a new understanding of our guistic patterns in textual corpora indicative of lives” (Lakoff & Johnson, 1980, p. 175). The potential conceptual metaphors. This work purpose of CMI, then, is to promote such criti- draws on and extends previous computational cal reflection by identifying particular linguis- linguistics research (Mason, 2004). tic patterns and presenting a reader with the Since metaphors map from a source do- metaphors those patterns might imply. main to a target domain, CMI begins by gath- ering corpora for the sources and target, where Metaphor in Computational Linguistics the target corpus is the text in which to identify metaphors, and the source corpora represent As described above, most

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