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Publication . Article . Other literature type . 2019

Re-Representing Metaphor: Modelling metaphor perception using dynamically contextual distributional semantics

Modeling metaphor perception using dynamically contextual distributional semantics
Stephen McGregor; Kat Agres; Karolina Rataj; Karolina Rataj; Matthew Purver; Geraint Wiggins; Geraint Wiggins;
Open Access
Published: 15 Apr 2019
Publisher: Frontiers Media S.A.
In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.
Subjects by Vocabulary

Microsoft Academic Graph classification: Distributional semantics Metaphor media_common.quotation_subject media_common Natural language processing computer.software_genre computer Psychology Word (computer architecture) Computational creativity Perception Artificial intelligence business.industry business Computational linguistics Space (commercial competition) Semantics

Library of Congress Subject Headings: lcsh:Psychology lcsh:BF1-990


General Psychology, Psychology, Original Research, distributional semantics, metaphor, conceptual models, computational creativity, vector space models, computational linguistics

61 references, page 1 of 7

Agres K.McGregor S.Purver M.Wiggins G. (2015). Conceptualising creativity: From distributional semantics to conceptual spaces, in Proceedings of the 6th International Conference on Computational Creativity (Park City, UT), 118–125.

Agres K. R.McGregor S.Rataj K.Purver M.Wiggins G. (2016). Modeling metaphor perception with distributional semantics vector space models, in Proceedings of the ESSLLI Workshop on Computational Creativity, Concept Invention, and General Intelligence (C3GI), eds Besold T. R.Kutz O.Leon C. (Bolzano-Bozen), 1–14.

Arzouan Y.Goldstein A.Faust M. (2007). Brainwaves are stethoscopes: ERP correlates of novel metaphor comprehension. Brain Res. 1160, 69–81. 10.1016/j.brainres.2007.05.034 17597591 [OpenAIRE] [PubMed] [DOI]

Bambini V.Arcara G.Bechi M.Buonocore M.Cavallaro R.Bosia M. (2016). The communicative impairment as a core feature of schizophrenia: Frequency of pragmatic deficit, cognitive substrates, and relation with quality of life. Comprehen. Psychiatry 71, 106–120. 10.1016/j.comppsych.2016.08.012 27653782 [OpenAIRE] [PubMed] [DOI]

Bambini V.Canal P.Resta D.Grimaldi M. (2019). Time course and neurophysiological underpinnings of metaphor in literary context. Discourse Proc. 56, 77–97. 10.1080/0163853X.2017.1401876 [DOI]

Barnden J. A. (2008). Metaphor and artificial intelligence: Why they matter to each other, in The Cambridge Handbook of Metaphor and Thought, ed Gibbs R. W. (New York, NY: Cambridge University Press), 311–338.

Barsalou L. W. (1999). Perceptions of perceptual symbols. Behav. Brain Sci. 22, 637–660. 10.1017/S0140525X99532147 [DOI]

Bowdle B. F.Gentner D. (2005). The career of metaphor. Psychol. Rev. 112:193. 10.1037/0033-295X.112.1.193 15631593 [OpenAIRE] [PubMed] [DOI]

Brouwer H.Crocker M. W.Venhuizen N. J.Hoeks J. C. (2017). A neurocomputational model of the n400 and the p600 in language processing. Cogn. Sci. 41, 1318–1352. 10.1111/cogs.12461 28000963 [OpenAIRE] [PubMed] [DOI]

Brouwer H.Hoeks J. C. (2013). A time and place for language comprehension: mapping the n400 and the p600 to a minimal cortical network. Front. Hum. Neurosci. 7:758. 10.3389/fnhum.2013.00758 24273505 [OpenAIRE] [PubMed] [DOI]

Funded by
ArTificial Language uNdersTanding In robotS
  • Project Code: ATLANTIS
Cross-Lingual Embeddings for Less-Represented Languages in European News Media
  • Funder: European Commission (EC)
  • Project Code: 825153
  • Funding stream: H2020 | RIA
Validated by funder
UKRI| DTA - Queen Mary, University of London
  • Funder: UK Research and Innovation (UKRI)
  • Project Code: EP/L50483X/1
  • Funding stream: EPSRC
Concept Creation Technology
  • Funder: European Commission (EC)
  • Project Code: 611733
  • Funding stream: FP7 | SP1 | ICT
Related to Research communities
Digital Humanities and Cultural Heritage
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