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Corpus Linguistics and Linguistic Theory

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Similarity is closeness: Using distributional semantic spaces to model similarity in visual and linguistic metaphors

Marianna Bolognesi
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  • Argumentation Theory and Rethorics, Universiteit van Amsterdam, Spuistraat 134, Amsterdam 1012VB, Netherlands
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/ Laura Aina
  • Institute for Logic, Language and Computation, Universiteit van Amsterdam, Amsterdam, Netherlands
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Published Online: 2017-02-24 | DOI: https://doi.org/10.1515/cllt-2016-0061


The semantic similarity that characterizes two terms aligned in a metaphor is here analysed through a corpus-based distributional semantic space. We compare and contrast two samples of metaphors, representative of visual and linguistic modality of expressions respectively. Popular theories of metaphor claim that metaphors transcend their modality to influence conceptual structures, thus suggesting that different modalities of expression would typically express the same conceptual metaphors. However, we show substantial differences in the degree of similarity captured by the distributional semantic space with regard to the modality of expression (higher similarity for linguistic metaphors than for visual ones). We argue that this is due to two possible variables: Conventionality (linguistic metaphors are typically conventional, while visual are not) and Complexity (visual metaphors have modality-specific inner complexities that penalize the degree of similarity between metaphor terms captured by a language-based model). Finally, we compare the similarity scores of our original formulations with those obtained from different possible verbalizations of the same metaphors (acquired by replacing the metaphor terms with their semantic neighbours). We show that while this operation does not affect the average similarity between metaphor terms for visual metaphors, the similarity changes significantly in linguistic metaphors. These results are discussed here.

Keywords: distributional semantics; metaphor analysis; multimodality


  • Artstein, Ron & Massimo Poesio. 2008. Inter-coder agreement for computational linguistics. Computational Linguistics 34(4). 555–596.Google Scholar

  • Baroni, Marco, & Alessandro Lenci. 2010. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics 36(4). 673–721.Google Scholar

  • Black, Max. 1979. More about metaphor. In Andrew Ortony (ed.), Metaphor and thought, 19–43. Cambridge: University Press.Google Scholar

  • Bolognesi, Marianna. 2016. Using semantic feature norms to investigate how the visual and the verbal modes afford metaphor construction and expression. Language and Cognition 27. 1–28.Google Scholar

  • Bolognesi, Marianna, Romy van den Heerik & Esther van den Berg. under review. VisMet: An online corpus of visual metaphors. In G. Steen (ed.), Visual metaphor: Structure and Process. Amsterdam: John Benjamins.Google Scholar

  • Bowdle, Brian & Dedre Gentner. 2005. The career of metaphor. Psychological Review 112. 193–216.Google Scholar

  • Deerwester, Scott, Susan Dumais & Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American society for information science. 41(6). 391–407.Google Scholar

  • Del Tredici, Marco & Nuria Bel. 2016. Assessing the potential of metaphoricity of verbs using corpus data. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), 4573–4577.

  • Firth, John Rupert. 1957. A synopsis of linguistic theory 1930–1955. Studies in Linguistic Analysis (special volume of the Philological Society) 1952–1959. 1–32.Google Scholar

  • Forceville, Charles. 1996. Pictorial metaphors in advertising. London: Routledge.Google Scholar

  • Forceville, Charles. 2005. Visual representations of the idealized cognitive model of anger in the Asterix album La Zizanie. Journal of Pragmatics 37. 69–88.Google Scholar

  • Forceville, Charles. 2011. The JOURNEY metaphor and the source-path-goal schema in Agnès Varda’s autobiographical gleaning documentaries. In Monika Fludernik (ed.), Beyond cognitive metaphor theory: Perspectives on literary metaphor, 281–297. London: Routledge.Google Scholar

  • Forceville, Charles & Eduardo Urios-Aparisi (eds.). 2009. Multimodal metaphor. Berlin: Mouton de Gruyter.Google Scholar

  • Giora, Rachel. 2008. Is metaphor unique? In Raymond Gibbs, Jr (ed.), The Cambridge handbook of metaphor and thought, 143–160. Cambridge, UK: University Press.Google Scholar

  • Glucksberg, Samuel. 2001. Understanding figurative language: From metaphors to idioms. New York: Oxford University Press.Google Scholar

  • Goodall, Catherine, Michael Slater & Teresa Myers. 2013. Fear and anger responses to local news coverage of alcohol-related crimes, accidents, and injuries: Explaining news effects on policy support using a representative sample of messages and people. Journal of Communication 63. 373–392.Google Scholar

  • Harris, Zellig. 1954. Distributional structure. Word 10(2). 146–162.Google Scholar

  • Hidalgo, Laura & Blanca Kraljevic. 2011. Multimodal metonymy and metaphor as complex discourse resources for creativity in ICT advertising discourse. In Francisco Gonzálvez García, Maria Sandra Peña & Lorena Pérez-Hernández (eds.), Metaphor and metonymy revisited beyond the contemporary theory of metaphor, 153–178. Amsterdam & Philadelphia: John Benjamins.Google Scholar

  • Jackendoff, Ray. 2002. Foundations of language. Oxford: University Press.Google Scholar

  • Kintsch, Walter. 2000. Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review 7. 257–266.Google Scholar

  • Lakoff, George & Mark Johnson. 1980. Metaphors we live by. Chicago: University Press.Google Scholar

  • Landauer, Thomas & Susan Dumais. 1997. A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological review 104(2). 211–240.Google Scholar

  • Lenci, Alessandro. 2008. Distributional semantics in linguistic and cognitive research. Italian journal of linguistics 20(1). 1–31.Google Scholar

  • McGlone, Matthew. 2007. What is the explanatory value of a conceptual metaphor? Language and Communication 27. 109–126.Google Scholar

  • Miller, George & Walter Charles. 1991. Contextual correlates of semantic similarity. Language and cognitive processes 6(1). 1–28.Google Scholar

  • Mitchell, William. 1994. Picture theory: Essays on verbal and visual representation. Chicago: University Press.Google Scholar

  • Murphy, Gregory. 1996. On metaphoric representation. Cognition 60(2). 173–204.Google Scholar

  • Ng, Carl & Veronika Koller. 2013. Deliberate conventional metaphor in images: The case of corporate branding discourse. Metaphor and Symbol 28(3). 131–147.CrossrefGoogle Scholar

  • Ortiz, Maria. 2011. Primary metaphors and monomodal visual metaphors. Journal of Pragmatics 43. 1568–1580.Google Scholar

  • Pérez Hernández, Lorena. 2014. Cognitive grounding for cross-cultural commercial communication. Cognitive Linguistics 25(2). 203–247.Google Scholar

  • Perez-Sobrino, Paula. 2016. Multimodal metaphor and metonymy in advertising: A corpus-based account. Metaphor and Symbol 31(2). 73–90.Google Scholar

  • Phillips, Barbara & Edward McQuarrie. 2004. Beyond visual metaphor: A new typology of visual rhetoric in advertising. Marketing Theory 4. 113–136.Google Scholar

  • Shutova, Ekaterina. 2015. Design and evaluation of metaphor processing systems. Computational Linguistics 41(1). 579–623.Google Scholar

  • Simmons, Joseph, Leif Nelson & Uri Simonsohn. 2011. False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science 22(11). 1359–1366.Google Scholar

  • Šorm, Ester & Gerard Steen. under review. VISMIP: Towards a method for visual metaphor Identification. In Gerard Steen (ed.), Visual metaphor: How images construct metaphorical meaning. Amsterdam: John Benjamins Publishing Company.Google Scholar

  • Steen, Gerard. 2013. Deliberate metaphor affords conscious metaphorical cognition. Journal of Cognitive Semiotics 5(1). 179–197.Google Scholar

  • Steen, Gerard, Lettie Dorst, Berenike Herrmann, Anna Kaal, Tina Krennmayr & Tryntje Pasma. 2010. A method for linguistic metaphor identification: From MIP to MIPVU. Amsterdam: John Benjamins.Google Scholar

  • Tukey, John. 1949. Comparing individual means in the analysis of variance. Biometrics 5. 99–114.Google Scholar

  • Turner, Mark & Gilles Fauconnier. 2002. The way we think. Conceptual blending and the mind’s hidden complexities. New York: Basic Books.Google Scholar

  • Turney, Peter. 2006. Similarity of semantic relations. Computational Linguistics 32(3). 379–416.Google Scholar

  • Turney, Peter & Patrick Pantel. 2010. From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research 37(1). 141–188.Google Scholar

  • Utsumi, Akira. 2011. Computational exploration of metaphor comprehension processes using a semantic space model. Cognitive Science 35(2). 251–296.Google Scholar

  • van Weelden, Lisanne, Alfons Maes, Joost Schilperoord & Marc Swerts. 2012. How object shape affects visual metaphor processing. Experimental Psychology 59(6). 364–371.Google Scholar

  • Veale, Tony, Ekaterina Shutova & Beata Klebanov. 2016. Metaphor: A computational perspective. Synthesis lectures on human language technologies. San Raphael, CA: Morgan and Claypool Publishers.Google Scholar

  • Vecchi, Eva Maria, Marco Baroni & Roberto Zamparelli. 2011. (Linear) maps of the impossible: Capturing semantic anomalies in distributional space. In Proceedings of the Workshop on Distributional Semantics and Compositionality, 1–9.

About the article

Published Online: 2017-02-24

Seventh Framework Programme, (Grant/Award Number: ‘FP7-PEOPLE-2013-IEF, COGVIM n° 629076’).

Citation Information: Corpus Linguistics and Linguistic Theory, ISSN (Online) 1613-7035, ISSN (Print) 1613-7027, DOI: https://doi.org/10.1515/cllt-2016-0061.

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