Skip to main content
×
×
Home

Cross-linguistic automated detection of metaphors for poverty and cancer

  • OANA DAVID (a1) and TEENIE MATLOCK (a1)
Abstract

Conceptual metaphor research has benefited from advances in discourse analytic and corpus linguistic methodologies over the years, especially given recent developments with Natural Language Processing (NLP) technologies. Such technologies are now capable of identifying metaphoric expressions across large bodies of text. Here we focus on how one particular analytic tool, MetaNet, can be used to study everyday discourse about personal and social problems, in particular, poverty and cancer, by leveraging reusable networks of primary metaphors enhanced with specific metaphor subcases. We discuss the advantages of this approach in allowing us to gain valuable insights into cross-linguistic metaphor commonalities and variation. To demonstrate its utility, we analyze corpus data from English and Spanish.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Cross-linguistic automated detection of metaphors for poverty and cancer
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Cross-linguistic automated detection of metaphors for poverty and cancer
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Cross-linguistic automated detection of metaphors for poverty and cancer
      Available formats
      ×
Copyright
Corresponding author
*Address for correspondence: Oana David, University of California, Merced, Cognitive and Information Sciences, 2500 North Lake Road, Merced, CA. e-mail: odavid@ucmerced.edu, oanadavid@gmail.com
Footnotes
Hide All
1

We are grateful to Ellen Dodge, Luca Gilardi, James Hieronymus, Jisup Hong, George Lakoff, Karie Moorman, Srini Narayanan, Jack Smith, Elise Stickles, Mahesh Srinivasan, and Eve Sweetser, who were members of either the MetaNet team or UC Berkeley’s Social Science Matrix 2015–2016 Metaphor Group. We would also like to thank our fellow UC Merced cancer metaphor researcher, Dalia Magaña. The research reported in this paper benefited from their input and contributions to the MetaNet project, which was located at the International Computer Science Institute, Berkeley in 2011–2016 (https://metanet.icsi.berkeley.edu/metanet/) and to the cancer metaphor project members at UC Merced and UC Berkeley.

The work presented here is a further development of work funded by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Defense US Army Research Laboratory contract number W911NF-12-C-0022. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoD/ARL, or the US Government.

Footnotes
References
Hide All
Barsalou, L. (1982). Context-independent and context-dependent information in concepts. Memory and Cognition 10(1), 8293.
Birke, J. & Sarkar, A. (2006). A clustering approach for the nearly unsupervised recognition of nonliteral language. Paper presented at the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006).
Burnes, S. (2011). Metaphors in press reports of elections: Obama walked on water, but Musharraf was beaten by a knockout. Journal of Pragmatics 43(8), 21602175.
Cahn, J. & Cahn, E. (1964). The war on poverty: a civilian perspective. Yale Law Journal 73(8), 13171352.
Cameron, L. (2010). ‘What is metaphor and why does it matter?’ in Cameron, L. & Maslen, R. (Eds.), Metaphor analysis: research practice in applied linguistics, social sciences and the humanities (pp. 325). London: Equinox.
Casarett, D., Pickard, A., Fishman, J. M., Alexander, S. C., Arnold, R. M., Pollak, K. I. & Tulsky, J. A. (2010). Can metaphors and analogies improve communication with seriously ill patients? Journal of Palliative Medicine 13(3), 255260.
David, O. (2017). Computational approaches to metaphor: the case of MetaNet. In Dancygier, B. (Ed.), The Cambridge handbook of cognitive linguistics (pp. 574589). Cambridge: Cambridge University Press.
David, O. A. (2016). Metaphor in the grammar of argument realization. Unpublished doctoral dissertation, University of California, Berkeley.
David, O. A., Lakoff, G. & Stickles, E. (2016). Cascades in metaphor and grammar: a case study of metaphors in the gun debate. Constructions and Frames 8(2), 165213.
Davies, M. (2013). Corpus of Global Web-Based English: 1.9 billion words from speakers in 20 countries. Available online at <http://corpus.byu.edu/glowbe/>.
Davies, M. & Fuchs, R. (2015). Expanding horizons in the study of World Englishes with the 1.9 billion word Global Web-based English Corpus (GloWbE). English World-Wide 36(1), 128.
Deignan, A. (2005). Metaphor and corpus linguistics. Amsterdam/Philadelphia: John Benjamins.
Deignan, A. (2010) The cognitive view of metaphor: Conceptual Metaphor Theory, in Cameron, L. & Maslen, R. (Eds.), Metaphor analysis: research practice in applied linguistics, social sciences and the humanities (pp. 4456). London: Equinox.
Demjén, Z., Semino, E. & Koller, V. (2016). Metaphors for ‘good’ and ‘bad’ deaths. Metaphor and the Social World 6(1), 119.
Demmen, J., Semino, E., Demjén, Z., Koller, V., Hardie, A., Rayson, P. & Payne, S. (2015). A computer-assisted study of the use of Violence metaphors for cancer and end of life by patients, family carers and health professionals. International Journal of Corpus Linguistics 20(2), 205231.
Do Dinh, E.-L. & Gurevych, I. (2016). Token-level metaphor detection using neural networks. Proceedings of the Fourth Workshop on Metaphor in NLP (June) (pp. 2833). Online: <http://www.aclweb.org/anthology/W16-1104>.
Dodge, E. K. (2016). A deep semantic corpus-based approach to metaphor analysis. Constructions and Frames 8(2), 256294.
Dodge, E. K., Hong, J. & Stickles, E. (2015). MetaNet: deep semantic automatic metaphor analysis. Proceedings of the Third Workshop on Metaphor in NLP (pp. 40–49). Denver, Colorado, 5 June 2015. Association for Computational Linguistics. Online: <http://www.aclweb.org/anthology/W15-1405>.
Dunn, J. (2013a). Evaluating the premises and results of four metaphor identification systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7816 LNCS(PART 1) (pp. 471486). Online: <https://link.springer.com/chapter/10.1007/978-3-642-37247-6_38>.
Dunn, J. (2013b). What metaphor identification systems can tell us about metaphor-in-language. Proceedings of the First Workshop on Metaphor in NLP, Atlanta Georgia, 13 June 2010 (pp. 110). Online: <http://www.aclweb.org/anthology/W13-0901>.
Elwood, W. N. (1995). Declaring war on the home front: metaphor, presidents and the war on drugs. Metaphor and Symbol 10(2), 93114.
Fellbaum, C. (1998). WordNet: an electronic lexical database. Cambridge: MIT Press.
Fillmore, C. J. & Atkins, B. T. (1992). Toward a frame-based lexicon: the semantics of RISK and its neighbors. In Lehrer, A. & Kittay, E. F. (Eds.), Frames, fields, and contrasts: new essays in semantic and lexical organization (pp. 75102). New York/London: Routledge.
Fillmore, C. J., Johnson, C. R. & Petruck, M. R. L. (2003). Background to FrameNet. International Journal of Lexicography 16(3), 235250.
Flusberg, S. J., Matlock, T. & Thibodeau, P. H. (2017). Metaphors for the war (or race) against climate change. Environmental Communication 11(6), 769783.
Flusberg, S. J., Matlock, T. & Thibodeau, P. H. (2018). War metaphors in public discourse. Metaphor and Symbol 33, 118.
Gedigian, M., Bryant, J., Narayanan, S. & Ciric, B. (2006). Catching metaphors. Proceedings of the Third Workshop on Scalable Natural Language Understanding ScaNaLU 06 (June), (pp. 4148). Online: <http://www1.icsi.berkeley.edu/~jbryant/GedigianBryantNarayananMetaphor.pdf>.
Gibbs, R. W. (2015). Counting metaphors: What does this reveal about language and thought? Cognitive Semantics 1, 155177.
Gibbs, R. W. Jr. (1994). The poetics of mind: figurative thought, language, and understanding. Cambridge: Cambridge University Press.
Gibbs, R. W. Jr. & Franks, H. (2002). Embodied metaphor in women’s narratives about their experiences with cancer. Health Communication 14(2), 139165.
Gibbs, R. W. Jr., Lima, P. L. C. & Francozo, E. (2004). Metaphor is grounded in embodied experience. Journal of Pragmatics 36(7), 11891210.
Gordon, J., Hobbs, J. R., May, J., Morbini, F. & Vista, P. (2015). High-precision abductive mapping of multilingual metaphors. Proceedings of the Third Workshop on Metaphor in NLP (2), 5055. Online: <http://www.aclweb.org/anthology/W15-1406>.
Grady, J. E. (1997). Foundations of meaning: primary metaphors and primary scenes. Unpublished doctoral dissertation, University of California, Berkeley.
Gutiérrez, D. E., Shutova, E., Marghetis, T. & Bergen, B. K. (2016). Literal and metaphorical senses in compositional distributional semantic models. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7–12, 2016 (pp. 183193). Online: <http://www.aclweb.org/anthology/P16-1018>.
Hong, J. (2016). Automatic metaphor detection using constructions and frames. Constructions and Frames 8(2), 293320.
Johnson, M. (1987). The body in the mind: the bodily basis of meaning, imagination, and reason. Chicago/London: University of Chicago Press.
Kövecses, Z. (2005). Metaphor in culture: universality and variation. Cambridge: Cambridge University Press.
Kövecses, Z. (2015). Where metaphors come from: reconsidering context in metaphor. Oxford: Oxford University Press.
Krishnakumaran, S. & Zhu, X. (2007). Hunting elusive metaphors using lexical resources. In Proceedings of the Workshop on Computational Approaches to Figurative Language (pp. 1320). Association for Computational Linguistics. Online: <https://dl.acm.org/citation.cfm?id=1611531>.
Lakoff, G. (1987). Women, fire and dangerous things: what categories reveal about thought. Chicago: University of Chicago Press.
Lakoff, G. (2012). Explaining embodied cognition results. Topics in Cognitive Science 4, 773785.
Lakoff, G. & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press.
Lakoff, G. & Johnson, M. (1999). Philosophy in the flesh. New York: Basic Books.
Lakoff, G. & Kövecses, Z. (1987). The cognitive model of anger inherent in American English. In Quinn, N. (Ed.), Cultural models in language and thought (pp. 195221). Cambridge: Cambridge University Press.
Lederer, J. (2013). Assessing claims of metaphorical salience through corpus data. In Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D. & Maglio, P. P. (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 12551260). Austin, TX: Cognitive Science Society.
Levin, L., Mitamura, T., Fromm, D., MacWhinney, B., Carbonell, J., Feely, W., Frederking, R., Gershman, A. & Ramirez, C. (2014). Resources for the detection of conventionalized metaphors in four languages. In Proceedings of the 9th International Conference on Language Resources and Evaluation (pp. 498501). Online: <https://pdfs.semanticscholar.org/1b08/69f556fc45c935b239447929b121762cac98.pdf>.
Lönneker, B. (2003). Is there a way to represent metaphors in WordNets? Insights from the Hamburg Metaphor Database. Proceedings of the ACL 2003 Workshop on Lexicon and Figurative Language – Volume 14 (pp. 1827). Online: <https://dl.acm.org/citation.cfm?id=1118978>.
MacWhinney, B. & Fromm, D. (2014). Two approaches to metaphor detection. Proceedings of the 9th Edition of the Language, Resources and Evaluation Conference (LREC 2014) (pp. 25012506). Online: <https://pdfs.semanticscholar.org/e8bc/8b3eeca8fe7146c1d830c4fff495c05b6568.pdf>.
Magaña, D. & Matlock, T. (2018). How Spanish speakers use metaphor to describe their experiences with cancer. Discourse & Communication. Available online <https://doi.org/10.1177/1750481318771446>.
Martin, J. H. (1988). A computational theory of metaphor. Unpublished doctoral dissertation, University of California, Berkeley.
Martin, J. H. (1994). MetaBank: a knowledge-base of metaphoric language conventions. Computational Intelligence 10(2), 134149.
Martin, J. H. (2006). A corpus-based analysis of context effects on metaphor comprehension. In Gries, S. T. & Stefanowitsch, A. (Eds.), Corpus-based approaches to metaphor and metonymy (pp. 214236). Berlin: Mouton de Gruyter.
Mason, Z. J. (2004). CorMet: a computational, corpus-based conventional metaphor extraction system. Computational Linguistics 30(1), 2344.
Mendonça, A., Jaquette, D., Graff, D. & DiPersio, D. (2011). Spanish Gigaword second edition (LDC2011T12). Philadelphia: Linguistic Data Consortium.
Mohler, M., Tomlinson, M. & Rink, B. (2015). Cross-lingual semantic generalization for the detection of metaphor. International Journal of Computational Linguistics and Applications 6(2), 117140.
National Cancer Institute (2017). National Cancer Act of 1971. Retrieved from <https://dtp.cancer.gov/timeline/flash/milestones/M4_Nixon.htm>.
Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS ONE 8(4), 19. Available online <https://doi.org/10.1371/journal.pone.0062343>.
Olweny, C. L. M. (1997). Effective communication with cancer patients: the use of analogies – a suggested approach. Annals of the New York Academy of Sciences 809, 179187.
Parker, R., Graff, D., Kong, J., Chen, K. & Maeda, K. (2011). English Gigaword fifth edition (LDC2011T07). Philadelphia: Linguistic Data Consortium.
Philip, G. (2004). Locating metaphor candidates in specialized corpora using raw frequency and keyword lists. In MacArthur, F., Oncins-Martínez, J. L., Sánchez-García, M. & Piquer-Píriz, A. M. (Eds.), Metaphor in use: context, culture, and communication (pp. 85105). Amsterdam: John Benjamins.
Pragglejaz Group (2007). MIP: a method for identifying metaphorically used words in discourse. Metaphor and Symbol 22(1), 139.
Radden, G. (2011). Spatial time in the West and the East. In Brdar, M., Omazić, M., Takač, V. P., Gradečak-Erdelijić, T. & Buljan, G. (Eds.), Space and time in language (pp. 1400). Frankfurt: Peter Lang.
Ruppenhofer, J. K., Ellsworth, M., Petruck, M. R. L., Baker, C. F. & Scheffczyk, J. (2016). FrameNet II: extended theory and practice. Berkeley, CA: International Computer Science Institute.
Semino, E. (2010). Descriptions of pain, metaphor, and embodied simulation. Metaphor and Symbol 25, 205226.
Semino, E., Demjén, Z., Demmen, J., Koller, V., Payne, S., Hardie, A. & Rayson, P. (2015). The online use of Violence and Journey metaphors by patients with cancer, as compared with health professionals: a mixed methods study. BMJ Supportive & Palliative Care 7(1), 17.
Semino, E., Heywood, J. & Short, M. (2004). Methodological problems in the analysis of metaphors in a corpus of conversations about cancer. Journal of Pragmatics 36(7), 12711294.
Semino, E. & Masci, M. (1996). Politics is football: metaphor in the discourse of Silvio Berlusconi in Italy. Discourse & Society 7(2), 243269.
Shutova, E. (2010). Models of Metaphor in NLP. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (July) (pp. 688697). Online: <https://dl.acm.org/citation.cfm?id=1858752>.
Shutova, E. & Sun, L. (2013). Unsupervised metaphor identification using hierarchical graph factorization clustering. In Proceedings of NAACL-HLT 2013, Atlanta, Georgia, 9–14 June 2013 (pp. 978988). Online: <http://www.aclweb.org/anthology/N13-1118>.
Shutova, E., Teufel, S. & Korhonen, A. (2012). Statistical metaphor processing. Computational Linguistics 39(2), 301353.
Small-McKinney, A. (2014). I Am Strong, I Am Not (The Breast Cancer Journey). Retrieved from <http://community.breastcancer.org/blog/i-am-strong-i-am-not/>.
Steen, G. J. (1999). From linguistic to conceptual metaphor in five steps. In Gibbs, R. W. & Steen, G. J. (Eds.), Metaphor in cognitive linguistics (pp. 5777). Amsterdam/Philadelphia: John Benjamins.
Steen, G. J., Biernacka, E., Dorst, A. G., Kaal, A. A., López-Rodríguez, I. & Pasma, T. (2010a). Pragglejaz in practice: finding metaphorically used words in natural discourse. In Low, G., Todd, Z., Deignan, A. & Cameron, L. (Eds.), Researching and applying metaphor in the real world (pp. 165184). Amsterdam/Philadelphia: John Benjamins.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A. & Krennmayr, T. (2010b). Metaphor in usage. Cognitive Linguistics 21, 765796.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A., Krennmayr, T. & Pasma, T. (2010c). A method for linguistic metaphor identification: from MIP to MIPVU. Amsterdam: John Benjamins.
Stefanowitsch, A. (2006). Words and their metaphors: a corpus-based approach. In Stefanowitsch, A. & Gries, S. T. (Eds.), Corpus-based approaches to metaphor and metonymy (pp. 63105). Berlin/NewYork: Mouton de Gruyter.
Stefanowitsch, A. & Gries, S. Th. (Eds.) (2006). Corpus based approaches to metaphor and metonymy. Berlin/New York: Mouton de Gruyter.
Stewart, M. (2014). The road to pain reconceptualisation: Do metaphors help or hinder the Journey? Pain and Rehabilitation: The Journal of Physiotherapy Pain Association 36, 2431.
Stickles, E., David, O., Dodge, E. K. & Hong, J. (2016). Formalizing contemporary conceptual metaphor theory. Constructions and Frames 8(2), 166213.
Sullivan, K. (2016). Integrating constructional semantics and conceptual metaphor. Constructions and Frames 8(2), 141165.
Sullivan, K. S. (2006). Frame-based constraints on lexical choice in metaphor. In Proceedings of the 32nd Annual Meeting of the Berkeley Linguistics Society (Vol. 32) (pp. 387400). Online: <https://journals.linguisticsociety.org/proceedings/index.php/BLS/article/viewFile/3476/3177>.
Sweetser, E., David, O. & Stickles, E. (in press). MetaNet: automated metaphor identification across languages and domains. In Bolognesi, M., Brdar, M. & Despot, K. S. (Eds.), Fantastic metaphors and where to find them: traditional and new methods in figurative language research. Amsterdam: John Benjamins.
Thibodeau, P. H. & Boroditsky, L. (2011). Metaphors we think with: the role of metaphor in reasoning. PLoS One 6(2), 111. Retrieved from <https://doi.org/10.1371/journal.pone.0016782>.
Thibodeau, P. H. & Boroditsky, L. (2013). Natural language metaphors covertly influence reasoning. PLoS ONE 8(1), 17. Retrieved from <https://doi.org/10.1371/journal.pone.0052961>.
Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E. & Dyer, C. (2014). Metaphor detection with cross-lingual model transfer. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014) (pp. 248258). Online: <http://www.aclweb.org/anthology/P14-1024>.
Weiss, M. (1997). Signifying the pandemics: metaphors of AIDS, cancer, and heart disease. Medical Anthropology Quarterly 11(4), 456476.
Wilks, Y. (1975). A preferential pattern-seeking semantics for natural language inference. Artificial Intelligence 6, 5374.
Wilks, Y. (1978). Making preferences more active. Artificial Intelligence 11(3), 197223.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Language and Cognition
  • ISSN: 1866-9808
  • EISSN: 1866-9859
  • URL: /core/journals/language-and-cognition
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed