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Language Resources and Evaluation

, Volume 47, Issue 4, pp 1261–1284 | Cite as

Conceptual metaphor theory meets the data: a corpus-based human annotation study

  • Ekaterina ShutovaEmail author
  • Barry J. Devereux
  • Anna Korhonen
Original Paper

Abstract

Metaphor makes our thoughts more vivid and fills our communication with richer imagery. Furthermore, according to the conceptual metaphor theory (CMT) of Lakoff and Johnson (Metaphors we live by. University of Chicago Press, Chicago, 1980), metaphor also plays an important structural role in the organization and processing of conceptual knowledge. According to this account, the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of individual words, but instead involves activating fixed mappings that reconceptualize one whole area of experience in terms of another. CMT produced a significant resonance in the fields of philosophy, linguistics, cognitive science and artificial intelligence and still underlies a large proportion of modern research on metaphor. However, there has to date been no comprehensive corpus-based study of conceptual metaphor, which would provide an empirical basis for evaluating the CMT using real-world linguistic data. The annotation scheme and the empirical study we present in this paper is a step towards filling this gap. We test our annotation procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The goal of the study is to investigate (1) how intuitive the conceptual metaphor explanation of linguistic metaphors is for human annotators and whether it is possible to consistently annotate interconceptual mappings; (2) what are the main difficulties that the annotators experience during the annotation process; (3) whether one conceptual metaphor is sufficient to explain a linguistic metaphor or whether a chain of conceptual metaphors is needed. The resulting corpus annotated for conceptual mappings provides a new, valuable dataset for linguistic, computational and cognitive experiments on metaphor.

Keywords

Conceptual metaphor theory Corpus annotation Human experimentation 

References

  1. Agerri, R., Barnden, J. A., Lee, M. G., & Wallington, A. M. (2007). Metaphor, inference and domain-independent mappings. In Proceedings of RANLP-2007 (pp. 17–23). Borovets, Bulgaria.Google Scholar
  2. Barnden, J. A., & Lee, M. G. (2002). An artificial intelligence approach to metaphor understanding. Theoria et Historia Scientiarum, 6(1), 399–412.Google Scholar
  3. Black, M. (1962). Models and metaphors. New York: Cornell University Press.Google Scholar
  4. Bowdle, B. F., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112, 193–216.CrossRefGoogle Scholar
  5. Burnard, L. (2007). Reference guide for the British National Corpus (XML ed.). http://www.natcorp.ox.ac.uk/XMLedition/URG/
  6. Cameron, L. (2003). Metaphor in educational discourse. London: Continuum.Google Scholar
  7. Charniak, E., Blaheta, D., Ge, N., Hall, K., Hale, J., & Johnson, M. (2000). BLLIP 1987-89 WSJ corpus release 1. Philadelphia: Linguistic Data Consortium.Google Scholar
  8. Chung, S. F., Ahrens, K., Huang, C. R. (2005). Source domains as concept domains in metaphorical expressions. International Journal of Computational Linguistics and Chinese Language Processing, 10(4), 553–570.Google Scholar
  9. Deignan, A. (2006). The grammar of linguistic metaphors. In Stefanowitsch, A., Gries, S. T. (eds.), Corpus-based approaches to metaphor and metonymy. Berlin: Mouton de Gruyter.Google Scholar
  10. Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind’s hidden complexities. New York: Basic Books.Google Scholar
  11. Feldman, J., & Narayanan, S. (2004). Embodied meaning in a neural theory of language. Brain and Language, 89(2), 385–392.CrossRefGoogle Scholar
  12. Feldman, J. A. (2006). From molecule to metaphor: A neural theory of language. Cambridge: The MIT Press.Google Scholar
  13. Gentner, D. (1983). Structure mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.CrossRefGoogle Scholar
  14. Gentner, D., Imai, M., & Boroditsky, L. (2002). As time goes by: Evidence for two systems in processing space-time metaphors. Language and Cognitive Processes, 47, 537–565.CrossRefGoogle Scholar
  15. Gibbs, R. (1984). Literal meaning and psychological theory. Cognitive Science, 8, 275–304.CrossRefGoogle Scholar
  16. Gibbs, R., & Tendahl, M. (2006). Cognitive effort and effects in metaphor comprehension: Relevance theory and psycholinguistics. Mind & Language, 21, 379–403.CrossRefGoogle Scholar
  17. Glucksberg, S. (2003). The psycholinguistics of metaphor. Trends in Cognitive Science, 7, 92–96.CrossRefGoogle Scholar
  18. Goatly, A. (1997). The language of metaphors. London: Routledge.Google Scholar
  19. Gong, S. P., Ahrens, K., & Huang, C. R. (2008). Chinese word sketch and mapping principles: A corpus-based study of conceptual metaphors using the BUILDING source domain. International Journal of Computer Processing of Oriental Languages, 21(2), 3–17.Google Scholar
  20. Grady, J. (1997). Foundations of meaning: Primary metaphors and primary scenes. Technical report, PhD thesis, University of California at Berkeley.Google Scholar
  21. Hardie, A., Koller, V., Rayson, P., & Semino, E. (2007). Exploiting a semantic annotation tool for metaphor analysis. In Proceedings of the corpus linguistics conference, Birmingham, UK.Google Scholar
  22. Haskell, R. E. (2002). Cognitive science and the origin of lexical metaphor. Theoria et Historia Scientiarum, 6(1), 291–331.Google Scholar
  23. Izwaini, S. (2003). Corpus-based study of metaphor in information technology. In Proceedings of the workshop on corpus-based approaches to figurative language, corpus linguistics 2003, Lancaster, 27 March 2003.Google Scholar
  24. Keysar, B., Shen, Y., Glucksberg, S., & Horton, W. S. (2000). Conventional language: How metaphorical is it? Journal of Memory and Language, 43, 576–593.CrossRefGoogle Scholar
  25. Koivisto-Alanko, P., & Tissari, H. (2006). Sense and sensibility: Rational thought versus emotion in metaphorical language. In Stefanowitsch, A., Gries, S. T. (Eds.), Corpus-based approaches to metaphor and metonymy. Berlin: Mouton de Gruyter.Google Scholar
  26. Krippendorff, K. (1980). Content analysis. Beverly Hills, CA: SAGE Publications.Google Scholar
  27. Krishnakumaran, S., & Zhu, X. (2007). Hunting elusive metaphors using lexical resources. In Proceedings of the workshop on computational approaches to figurative language (pp. 13–20). Rochester, NY.Google Scholar
  28. Lakoff, G. (1992). The contemporary theory of metaphor. In Ortony, A. (Ed.), Metaphor and thought (2nd ed., pp. 202–251). Cambridge, UK: Cambridge University Press.Google Scholar
  29. Lakoff, G., Espenson, J., & Schwartz, A. (1991). The master metaphor list. Technical report, University of California at Berkeley.Google Scholar
  30. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press.Google Scholar
  31. Landis, J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.CrossRefGoogle Scholar
  32. Lönneker, B. (2004). Lexical databases as resources for linguistic creativity: Focus on metaphor. In Proceedings of the LREC 2004 workshop on language resources for linguistic creativity (pp. 9–16). Lisbon, Portugal.Google Scholar
  33. Lönneker-Rodman, B. (2008). The hamburg metaphor database project. Issues in Resource Creation Language Resources and Evaluation, 42, 293–318.CrossRefGoogle Scholar
  34. Lönneker, B., & Eilts, C. (2004). A current resource and future perspectives for enriching wordnets with metaphor information. In Proceedings of the second international wordnet conference (GWC 2004) (pp. 157–162). Brno, Czech Republic, 2004.Google Scholar
  35. Low, G., Todd, Z., Deignan, A., & Cameron, L. (2010). Researching and applying metaphor in the real world. Amsterdam/Philadelphia: John Benjamins.Google Scholar
  36. Lu, L., & Ahrens, K. (2008). Ideological influences on BUILDING metaphors in Taiwanese presidential speeches. Discourse and Society, 19(3), 383–408.CrossRefGoogle Scholar
  37. Martin, J. H. (1988). Representing regularities in the metaphoric lexicon. In Proceedings of the 12th conference on computational linguistics (pp. 396–401).Google Scholar
  38. Martin, J. H. (1990). a computational model of metaphor interpretation. San Diego, CA, USA: Academic Press Professional.Google Scholar
  39. Martin, J. H. (1994). Metabank: A knowledge-base of metaphoric language conventions. Computational Intelligence, 10, 134–149.CrossRefGoogle Scholar
  40. Martin, J. H. (2006). A corpus-based analysis of context effects on metaphor comprehension. In Stefanowitsch, A., Gries, S. T. (Eds.), Corpus-Based approaches to metaphor and metonymy. Berlin: Mouton de Gruyter.Google Scholar
  41. Mason, Z. J. (2004). Cormet: A computational, corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44.CrossRefGoogle Scholar
  42. McGlone, M. S. (2007). What is the explanatory value of a conceptual metaphor? Language & Communication, 27, 109–126.CrossRefGoogle Scholar
  43. Murphy, G. L. (1996). On metaphoric representation. Cognition, 60, 173–204.CrossRefGoogle Scholar
  44. Narayanan, S. (1997). Knowledge-based action representations for metaphor and aspect (KARMA). Technical report, PhD thesis, University of California at Berkeley.Google Scholar
  45. Narayanan, S. (1999). Moving right along: A computational model of metaphoric reasoning about events. In Proceedings of AAAI 99) (pp. 121–128). Orlando, Florida.Google Scholar
  46. Pinker, S. (2007). The stuff of thought: Language as a window into human nature. USA: Viking Adult.Google Scholar
  47. Pragglejaz Group. (2007). MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22, 1–39.Google Scholar
  48. Reddy, M. (1978). The conduit metaphor: A case of frame conflict in our language about language. In: Ortony, A., (Ed.), Metaphor and thought (2nd ed., pp. 164–201). Cambridge, UK: Cambridge University Press.Google Scholar
  49. Shalizi, C. R. (2003). Analogy and metaphor. http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/analogy.html
  50. Shutova, E. (2010). Models of metaphor in NLP. In Proceedings of ACL 2010, Uppsala, Sweden.Google Scholar
  51. Shutova, E., & Teufel, S. (2010). Metaphor corpus annotated for source-target domain mappings. In Proceedings of LREC 2010, Valletta, Malta.Google Scholar
  52. Shutova, E., Sun, L., & Korhonen, A. (2010). Metaphor identification using verb and noun clustering. In Proceedings of Coling 2010, Beijing, China.Google Scholar
  53. Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics for the behavioral sciences. New York, USA: McGraw-Hill Book Company.Google Scholar
  54. Skorczynska Sznajder, H., & Pique-Angordans, J. (2004). A corpus-based description of metaphorical marking patterns inscientific and popular business discourse. In Proceedings of European research conference on mind, language and metaphor (Euresco conference), Granada, Spain.Google Scholar
  55. Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A., Krennmayr, T., & Pasma, T. (2010). A method for linguistic metaphor identification: From MIP to MIPVU. Amsterdam/Philadelphia: John Benjamins.Google Scholar
  56. Wallington, A. M., Barnden, J. A., Buchlovsky, P., Fellows, L., & Glasbey, S. R. (2003). Metaphor annotation: A systematic study. Technical report, School of Computer Science, The University of Birmingham.Google Scholar
  57. Wikberg, K. (2008). The role of corpus studies in metaphor research. In N. L. Johannesson & D. C. Minugh (Eds.), Selected Papers from the 2006 and 2007 Stockholm Metaphor Festivals (pp. 33–48). Stockholm: Department of English, Stockholm University.Google Scholar
  58. Wilks, Y. (1975). A preferential pattern-seeking semantics for natural language inference. Artificial Intelligence, 6, 53–74.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ekaterina Shutova
    • 1
    Email author
  • Barry J. Devereux
    • 2
  • Anna Korhonen
    • 1
  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK

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