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Cross-linguistic automated detection of metaphors for poverty and cancer



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.

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Corresponding author

*Address for correspondence: Oana David, University of California, Merced, Cognitive and Information Sciences, 2500 North Lake Road, Merced, CA. e-mail:,


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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 ( 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.



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