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 constructed 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 previous com- source domains for the metaphors one might putational linguistics research on metaphor has wish to identify. The researcher selects the focused on discerning figurative from literal source domain based on a theoretical predic- phrases and then determining the literal mean- tion that this may be a likely source for the ing of figurative language (Fass, 1991; Lu & identified target domain. For example, the do- Feldman, 2007; Martin, 1990). For example, main LIQUID might be identified as a poten- one application, designed as a help system for tial source for the target concept money. This the UNIX command line, would determine that approach treats domain representations as less statements such as “how do I get into Lisp?” formally structured than the structure-mapping meant the user wanted to know how to invoke (Gentner, 1983) or multi-constraint satisfaction Lisp programming environment (Martin, (Holyoak & Thagard, 1989) models of anal- 1990). One exception to this trend is CorMet ogy. This flexibility allows for consideration (Mason, 2004), a system designed to extract of more dispersed, subtle infiltration of cross- known conceptual metaphors from domain- domain mappings into everyday thought. specific textual corpora. For example, CorMet This paper describes results from analyz- was used to extract the metaphor MONEY IS A ing two target corpora: a collection of middle- LIQUID by using documents acquired from web school students' writing about cellular biology,

22 Computational Metaphor Identification and a set of political blogs. The implementa- classes of nouns. Thus, individual nouns count tion described here uses Wikipedia articles for as partial observations of each class of words source corpora, as they provide a readily avail- that they might represent, using the ontological able, categorically sorted, large source of text dictionary WordNet (Fellbaum, 1998). For on a wide variety of topics. A source corpus example, the words “water,” “liquor,” and for a given domain consists of all the Wikipe- “ammonia” can all represent the concept of dia articles in the category for that domain, as liquid, as liquid is a parent, or hypernym, of well as all articles in its subcategory. All each in the WordNet hierarchy. WordNet uses documents in the source and target corpora are synsets (sets of synonyms) to represent classes parsed to extract grammatical relationships (de of words. For example, the synonyms “liquid,” Marneffe, MacCartney, & Manning, 2006). “liquidness,” “liquidity,” and “liquid state” The crux of CMI is selectional preference comprise the synset for the liquid state of mat- learning (Resnik, 1993), which identifies asso- ter. These synsets are then clustered using two- ciation between different classes of words nearest-neighbor clustering based on the verbs through specific grammatical relationships. for which they select. Each cluster represents a For example, words for the concept of food are coherent concept in the corpus. often the direct object of the verb “eat.” Selec- This approach of clustering hypernyms tional preferences are often calculated in terms resonates with Lakoff's argument that meta- of verbs’ preferences for nouns, but they can phorical mappings occur not at the level of just as readily be calculated in terms of nouns’ situational specifics, but at the superordinate preferences for verbs (Light & Greiff, 2002). level. For example, in the metaphor LOVE IS A Using the parsed documents, CMI calculates JOURNEY, the relationship between the lovers selectional preferences of the characteristic is the vehicle in which they travel. Although nouns in a corpus, where characteristic means specific instantiations of the metaphor may that the noun is highly frequent in the corpus frame that vehicle variously as a train (“off the relative to its frequency in general English. track”), a car (“long, bumpy road”), or a plane Selectional preference is quantified as the rela- (“just taking off”), “the categories mapped will tive entropy of the posterior distribution condi- tend to be at the superordinate level rather than tioned on a specific noun and grammatical the basic level” (Lakoff, 1993, p. 212). The case slot with respect to the prior distribution method described here causes observations at of verbs in general English: the basic level to accumulate in the superordinate levels they collectively represent. To identify metaphors, CMI looks for selectional correspondences between conceptual where c is a class of nouns (e.g., nouns repre- clusters in the source and target corpora. For senting the concept food) and a case slot, and v example, in the LIQUID domain, the cluster ranges over all the verbs for which c appears in for the concept container would have strong the given case slot. Selectional preference cap- selectional associations for the object of the tures the “choosiness” of a particular gram- preposition “into” with the verb “pour,” the matical relationship; selectional association object of the preposition “from” with the verb measures the degree to which that grammatical “flow,” the subject of the verb “hold,” and so relationship is associated with a particular on. In documents about banking or finance, the verb: cluster for institution also selects for those same verbs in the same grammatical relationships. Based on the degree of correspondence between those selectional associations, each While selectional associations are calcu- cluster-to-cluster mapping is given a confi- lated for classes of words, corpora consist of dence score indicating how likely the linguistic words that may represent many possible patterns are to evidence a conceptual meta-

23 Eric Baumer, Bill Tomlinson, and Lindsey Richland phor. One of CMI's strengths is that it works in corpora. The first is a collection of science the aggregate. While individual phrases such students' written answers to questions about as “flowed from the Federal Reserve” and cellular reproduction. The second is a set of “poured money into my IRA” may not at first posts from political blogs during the 2008 US glance appear metaphorical, it is the sys- election. The results from these two rather tematicity of these patterns that becomes com- different corpora demonstrate CMI's ability to pelling evidence for the existence of a meta- identify potential metaphors in a wide variety phor. of content. Both corpora are part of larger re- This mapping of selectional associations search projects that use CMI to foster critical is reminiscent of the selectional restrictions thinking and reflection. violations view of metaphor (cf. Fass, 1991), also called the “anomaly view” (Tourangeau & Science Students' Writing Sternberg, 1982), wherein a metaphor can be identified when semantic constraint expecta- The first corpus comes from 7th grade tions are violated. For example, in the phrase (ages 12-13) science students' written answers “my car drinks gasoline,” cars do not usually to questions about cellular reproduction. Open- drink, and gasoline is not usually drunk; thus, ended questions were asked before and during two selectional restriction violations indicate a the model, such as: “What are some differ- potential metaphor. However, as Ortony (Or- ences between mitosis and meiosis?”; or, “Do tony, 1980) and others have pointed out, such you think offspring of ALL organisms are al- violations are highly contextually dependent. ways different from their parents? Why or why Thus, rather than attempting to derive general- not?.” Answers to these questions were col- purpose selectional associations, CMI maps lected and analyzed using CMI to determine selectional associations from a specific source what metaphors students might be invoking. corpus, the original context of use, to a spe- The corpus consisted of approximately 20,000 cific target corpus, in which constraints de- words, about 2,000 sentences, and 15,000 rived from the original context of use may be grammatical relations. seen as violated. Students' answers were analyzed for An important aspect of CMI is that it metaphors from the ARCHITECTURE do- identifies only linguistic patterns potentially main. This source domain choice was in- indicative of conceptual metaphors, not the formed partly by prior work describing the metaphors themselves. As Lakoff (1993) em- metaphor BODIES ARE BUILDINGS (Lakoff, phasizes, metaphor is primarily a cognitive Espenson, & Schwartz, 1991) and partly by the phenomenon, and metaphorical language common instructional metaphor CELLS ARE serves as evidence for the cognitive phenome- BUILDINGS BLOCKS in the body. Table 1 shows non. CMI leverages computational power to computationally identified metaphors for the search through large bodies of text in order to concept cell. The “Conf” column indicates the identify patterns of potential interest, then pre- confidence score assigned by CMI to the po- sents those patterns to a human user along with tential metaphor. These scores typically fall in the potential metaphors they might imply. This the range 0 to 10 and follow a roughly expo- approach places the job of finding patterns in nential distribution, with a few high- the hands of the computer, and the job of in- confidence mappings and many low- terpreting those patterns in the hands of the confidence mappings. The metaphors shown human user. here are in the upper one percentile in terms of confidence. The “Cell is Like” column indi- SAMPLE RESULTS cates the source concept from the ARCHI- TECTURE domain mapping to cell, i.e., a cell This section presents sample portions of “is like” the contents of this column. To reiter- results from applying CMI to two different ate, the source and target concepts are repre-

24 Computational Metaphor Identification sented by the automatically identified clusters tial conceptual framings. The “#” column of synsets. The “Target Example Fragments” shows the total number of sentences from each and “Source Example Fragments” columns corpus acting as evidence for the metaphor show example sentence fragments (with errors with each verb-case slot. These mappings are unaltered) that serve as evidence for the meta- each mediated by six to nine verb-case slots; in phor in the target and source corpora, respec- the interest of space, only three are shown for tively, matched by verb-case slot. While the each mapping. examples may not seem individually compel- The example sentences demonstrate ling, in the aggregate they can suggest poten- CMI's adherence to the idea that mappings

25 Eric Baumer, Bill Tomlinson, and Lindsey Richland occur at the level of superordinate concepts but were then incorporated back into latter parts of individual instances occur with respect to situ- the module. For three such metaphors, students ational specifics. For example, in the A CELL IS were asked if the metaphor made sense and if A MATERIAL mapping, the three example frag- it was similar to their thinking. They were also ments shown here do not involve the word asked how the metaphor might not fit, e.g., “material” but rather specific instances of ma- how a cell might not be like a building or a terials: “hydrogen,” “aggregate,” and “iron.” structure. Responses to these questions are The strongest metaphor, A CELL IS A MA- being analyzed to determine not only the TERIAL, helps confirm that CMI behaves as amount but also the kinds of critical thinking one might expect, resonating with the common that CMI can support. Do students focus more instructional metaphor CELLS ARE BUILDING on aspects of the source or the target concept. BLOCKS, a metaphor implicit in the educational Do they focus on surface features or structural module. The identified metaphor A CELL IS A aspects? Surface features arguably play a PIECE also aligns with this overall conceptual prominent role as a retrieval cue for generating framing that cells are component pieces of a metaphors (e.g., Holyoak & Koh, 1987), but larger whole. On the other hand, the identified what role do they play in assessing potential metaphors A CELL IS A BUILDING and A CELL IS metaphors? A STRUCTURE suggest a somewhat different conceptualization, wherein a cell is not a part Political Blogs of a larger system but is a system in its own right, possibly indicating an alternative way in The second corpus consists of posts from which students are invoking the BODIES ARE 11 political blogs, all with an express Republi- BUILDINGS (Lakoff et al., 1991) metaphor. can bias, collected during the 2008 US elec- Two more identified metaphors, A CELL tion. These posts totaled approximately IS A STYLE and A CELL IS A DESIGN, suggest a 100,000 words, 5,400 sentences, and 16,600 number of novel, creative relational compari- grammatical relations. sons. First, rather than a component in a con- These blog posts were analyzed for meta- struction process, a cell and the DNA it con- phors from the SCIENCE domain. While po- tains could be seen as a specific style or design litical metaphors often draw on the domains of for how to build an organism, where one cell WAR or SPORTS (Howe, 1988), SCIENCE may “combine with” another to form the de- was chosen to explore CMI's ability to find sign of a slightly different organism. Second, metaphors from non-obvious source domains. since all living things are “made from” cells, Table 2 lists the strongest identified metaphors the very notion of a cell could be seen as a in this corpus, those for the concept of candi- style or design pattern from which nature is date, using the same format as Table 1. constructed. Third, this metaphor draws atten- These metaphors provide two novel and tion to a potential misconception about a cell's potentially compelling framings of a political agency. Styles and designs imply stylers and candidate in an election. First, the metaphor A designers with intentionality and agency, both CANDIDATE IS A SCIENTIST provides a new po- of which have the potential to produce mis- tential framing to an election, e.g., scientists conceptions when applied to understanding may be criticized for their ideas, just as candi- cellular reproduction. Thus, CMI can provide dates might be criticized for policy sugges- opportunities to examine critically potential tions. Second, and somewhat more compel- metaphors and to determine ways in which ling, are the other four metaphors shown here: they might not fit the target concept.This ap- A CANDIDATE IS A THEORY, A CANDIDATE IS AN proach has been applied to the design of an- IDEA, A CANDIDATE IS A STUDY, and A CANDI- other computer-based educational module. DATE IS A HYPOTHESIS. These indicate the way Students' written answers were analyzed for that a candidate represents a potentiality, a metaphors, and some identified metaphors theory or an idea to be “tested,” something that

26 Computational Metaphor Identification is not yet fully known, is not yet proven. Some phors that might not be readily apparent but theories work well in some situations but not can provide compelling, novel reconceptuali- in others, just as candidates may have zations of familiar ideas or situations. strengths and weaknesses in their campaigns. These and similar results are being used A voter might “support” one candidate while in a tool designed for readers of political blogs “criticizing” another, just as a scientist might (cf. Baumer, Sueyoshi, & Tomlinson, 2008). “support” one hypothesis while “criticizing” metaViz [http://metaviz.ics.uci.edu] visually another. Ultimately, one candidate is proven in presents potential metaphors in a variety of an election just as one or another theory might blogs and allows users to comments on the be proven through experimental validation. metaphors. Rather than focusing on individual Rather than a war, a battle, or a contest, these posts, metaViz draws readers' attention to pat- four metaphors provide a new way of seeing terns that span many posts or multiple blogs. an election, framing it as a scientific process of This tool encourages readers to consider not experimentation wherein candidates do not try only what is being said by the words them- to defeat one another but rather try to prove selves, but between and behind the words. themselves through various tests. These map- The metaViz system is currently being pings demonstrate CMI's ability to identify evaluated from two perspectives. First, a con- linguistic patterns indicative of potential meta- trolled experimental study was recently con-

27 Eric Baumer, Bill Tomlinson, and Lindsey Richland ducted comparing use of metaViz to reading Acknowledgments blogs alone to determine differences in critical thinking and creativity. These results indicate This material is based upon work sup- that subjects who used metaViz did not exhibit ported by the National Science Foundation a greater amount of critical thinking, but they under Grant No. IIS-0757646, the Donald did demonstrate wider variety in the ways they Bren School of Information and Computer critically examined potential metaphors (Bau- Sciences, and the California Institute for Tele- mer, Sinclair, Hubin, & Tomlinson, 2009). The communication and Information Technology second evaluation involves a long-term, in situ (Calit2). The authors also thank the Social study with a small pilot group of political blog Code Group and The Learning and Cognition readers, the goal of which is to understand how Lab for their feedback and advice. metaViz integrates with daily blog-reading practices, how it changes readers' perceptions References of language and blogs, and how it might en- able new ways of reading. Baumer, E., Sinclair, J., Hubin, D., & Tomlinson, B. (2009). metaViz: Visualiz- CONCLUSION ing Computationally Identified Meta- phors in Political Blogs. In IEEE Interna- This paper described computational tional Symposium on Social Computing metaphor identification (CMI), a technique for (SocialCom). Vancouver, BC, Canada: identifying potential conceptual metaphors in IEEE Computer Society. written text. Unlike other computational mod- Baumer, E., Sueyoshi, M., & Tomlinson, B. els of analogical thinking, CMI does not use (2008). Exploring the Role of the Reader encoded representations of knowledge struc- in the Activity of Blogging. In ACM tures, but rather identifies potential mappings SIGCHI Conf (pp. 1111-1120). Florence, based on cross-corpus linguistic correspon- Italy: ACM. dences. The sample results presented here Doumas, L. A. A., Hummel, J. E., & Sand- demonstrate the potential of using CMI to hofer, C. M. (2008). A Theory of the promote critical and creative thinking about Discovery and Predication of Relational conceptual metaphors. Concepts. Psychological Review, 115(1), The work described here represents a 1-43. new opportunity for research on relational Falkenhainer, B., Forbus, K., & Gentner, D. thinking. Lakoff argues that “issues [about (1989). The Structure-Mapping Engine: metaphor] are not matters for definitions; they Algorithm and Examples. Artificial Intel- are empirical questions” (1993, p. 202). How- ligence, 41, 1-63. ever, one common critique of such cognitive Fass, D. (1991). Met*: A method for discrimi- linguistics work is its lack of clarity with re- nating metonymy and metaphor by com- spect to methods, especially its empirical puter. Comp Ling, 17(1), 49-90. grounding (Gibbs, 2007). By examining sys- Fellbaum, C. (1998). WordNet: An Electronic temic patterns in large bodies of text, compu- Lexical Database. Cambridge, MA: MIT tational metaphor identification has the poten- Press. tial to help fill that gap, providing a new Gentner, D. (1983). Structure-mapping: A method for empirical investigation of rela- theoretical framework for analogy. Cog- tional thinking in everyday language and nitive Science, 7, 155-170. thought. Hall, R. P. (1989). Computational approaches to analogical reasoning: A comparative analysis. Artificial Intelligence, 39(1), 39-120. doi: 10.1016/0004- 3702(89)90003-9.

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