Chapter published in:
Producing Figurative Expression: Theoretical, experimental and practical perspectives
Edited by John Barnden and Andrew Gargett
[Figurative Thought and Language 10] 2020
► pp. 419448


Agres, K. R., McGregor, S., Rataj, K., Purver, M., & Wiggins, G. A.
(2016) Modeling metaphor perception with distributional semantics vector space models. In Workshop on Computational Creativity, Concept Invention, and General Intelligence .
Barnden, J. A., & Lee, M. G.
(1999) An implemented context system that combines belief reasoning, metaphor-based reasoning and uncertainty handling. In Modeling and Using Context: Second International and Interdisciplinary Conference (pp. 28– 41).
Baroni, M., & Zamparelli, R.
(2010) Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (pp. 1183– 1193).
Barsalou, L. W.
(1992) Frames, concepts, and conceptual fields. In A. Lehrer, & E. F. Kittay (Eds.), Frames, fields, and contrasts: New essays in semantic and lexical organization (pp. 21–74). Hillsdale, N.J.: Lawrence Erlbaum Associates.Google Scholar
(1993) Flexibility, structure, and linguistic vagary in concepts: manifestations of a compositional system of perceptual symbols. In A. Collins, S. Gathercole, & M. Conway (Eds.), Theories of memory (pp. 29–101). London: Lawrence Erlbaum Associates.Google Scholar
Bellman, R. E.
(2003) Dynamic programming. Dover Publications, Incorporated.Google Scholar
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. CrossrefGoogle Scholar
Carston, R.
(2002) Metaphor, ad hoc concepts and word-meaning – more questions than answers. UCL Working Papers in Linguistics, 14, 83–105.Google Scholar
(2010) Metaphor: Ad hoc concepts, literal meaning and mental images. Proceedings of the Aristotelian Society, 110(3), 297–323.Google Scholar
(2012) Metaphor and the literal/nonliteral distinction. In K. Allan, & K. M. Jaszczolt (Eds.), The Cambridge handbook of pragmatics (pp. 469–492). Cambridge University Press. CrossrefGoogle Scholar
Chomsky, N.
(1957) Syntactic structures. The Hague: Mouton and Co. CrossrefGoogle Scholar
Clark, A.
(2006) Language, embodiment, and the cognitive niche. Trends in Cognitive Sciences, 10(8), 370–374. CrossrefGoogle Scholar
Clark, S.
(2015) Vector space models of lexical meaning. In S. Lappin, & C. Fox (Eds.), The handbook of contemporary semantic theory (pp. 493–522). Wiley-Blackwell. CrossrefGoogle Scholar
Coecke, B., Sadrzadeh, M., & Clark, S.
(2011) Mathematical foundations for a compositional distributed model of meaning. Linguistic Analysis, 36(1–4), 345–384.Google Scholar
Davidson, D.
(1978) What metaphors mean. In Inquiries into truth and interpretation (2nd ed.). Oxford: Clarendon Press.Google Scholar
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K.
(2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186).
Ethayarajh, K. (2019) How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 55–65).
Fauconnier, G., & Turner, M. B.
(2008) Rethinking metaphor. In R. Gibbs (Ed.), Cambridge handbook of metaphor and thought. Cambridge University Press. CrossrefGoogle Scholar
Gärdenfors, P.
(2000) Conceptual space: The geometry of thought. Cambridge, MA: The MIT Press. CrossrefGoogle Scholar
Gargett, A., & J. Barnden (2013) Gen-meta: Generating metaphors using a combination of AI reasoning and corpus-based modeling of formulaic expressions. In Proceedings of TAAI 2013 .
Gentner, D.
(1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. CrossrefGoogle Scholar
Gibbs, R. W.,Jr.
(1994) The poetics of mind. Cambridge University Press.Google Scholar
Gibbs Jr., R. W., & Tendahl, M.
(2006) Cognitive effort and effects in metaphor comprehension: Relevance theory and psycholinguistics. Mind and Language, 21(3), 379–403. CrossrefGoogle Scholar
Gibson, J. J.
(1979) The ecological approach to visual perception. Boston: Houghton Miffline.Google Scholar
Grefenstette, E., Sadrzadeh, M., Clark, S., Coecke, B., & Pulman, S.
(2014) Concrete sentence spaces for compositional distributional models of meaning (pp. 71–86). Dordrecht: Springer Netherlands.Google Scholar
Grice, H. P.
(1975) Logic and conversation. In P. Cole, & J. L. Morgan (Eds.), Syntax and semantics volume 3: Speech acts (pp. 41–58). New York: Academic Press.Google Scholar
Gutiérrez, E. D., Shutova, E., Marghetis, T., & Bergen, B. K.
(2016) Literal and metaphorical senses in compositional distributional semantic models. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics . Crossref
Harris, Z.
(1954) Distributional structure. Word, 10(23), 146–162. CrossrefGoogle Scholar
Kartsaklis, D., & Sadrzadeh, M.
(2013) Prior disambiguation of word tensors for constructing sentence vectors. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1590–1601).
Kintsch, W.
(2000) Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 7(2), 257–266. CrossrefGoogle Scholar
Lakoff, G., & Johnson, M.
(2003) Metaphors we live by (2nd ed.). University of Chicago Press. CrossrefGoogle Scholar
Levinson, S. C.
(1996) Relativity in spatial conception and description. In J. J. Gumperz, & S. C. Levinson (Eds.), Rethinking linguistic relativity (pp. 177–202). Cambridge University Press.Google Scholar
Mason, Z. J.
(2004) Cormet: A computational, corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44. CrossrefGoogle Scholar
McGregor, S., Agres, K., Purver, M., & Wiggins, G.
(2015) From distributional semantics to conceptual spaces: A novel computational method for concept creation. Journal of Artificial General Intelligence. CrossrefGoogle Scholar
McGregor, S., Jezek, E., Purver, M., & Wiggins, G.
(2017) A geometric method for detecting semantic coercion. In Proceedings of 12th International Workshop on Computational Semantics .
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) Efficient estimation of word representations in vector space. In Proceedings of ICLR Workshop .
Miyazawa, A., & Miyao, Y.
(2017) Evaluation metrics for automatically generated metaphorical expressions. In 12th International Workshop on Computational Semantics .
Pantel, P.
(2005) Inducing ontological co-occurrence vectors. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA (pp. 125–132). Association for Computational Linguistics.Google Scholar
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L.
(2018) Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 2227–2237).
Reimer, M.
(2001) Davidson on metaphor. Midwest Studies in Philosophy, 25, 142–155. CrossrefGoogle Scholar
(2013) Grice on irony and metaphor: Discredited by the experimental evidence? International Review of Pragmatics, 5(1), 1–33. CrossrefGoogle Scholar
Salton, G., Wong, A., & Yang, C. S.
(1975) A vector space model for automatic indexing. In Proceedings of the 12th ACM SIGIR Conference (pp. 137–150).
Schütze, H.
(1992) Dimensions of meaning. In Proceedings of the 1992 ACM/IEEE conference on Supercomputing (pp. 787–796).
Searle, J. R.
(1979) Metaphor. In A. Ortony (Ed.), Metaphor and thought. Cambridge University Press.Google Scholar
Shutova, E.
(2015) Design and evaluation of metaphor processing systems. Computational Linguistics, 41(4), 579–623. CrossrefGoogle Scholar
Shutova, E., Teufel, S., & Korhonen, A.
(2013) Statistical metaphor processing. Computational Linguistics, 39(2), 301–353. CrossrefGoogle Scholar
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A., Krennmayr, T., & Pasma, T.
(2010) A method for linguistic metaphor identification: From MIP to MIPVU. Converging Evidence in Language and Communication Research. Amsterdam: John Benjamins Publishing Company. CrossrefGoogle Scholar
Stickles, E., David, O., Dodge, E. K., & Hong, J. (2016) Formalizing contemporary conceptual metaphor theory: A structured repository for metaphor analysis. MetaNet, Special Issue of Constructions and Frames, 8(2).Google Scholar
Sweetser, E.
(1990) From etymology to pragmatics: Metaphor and cultural aspects of semantic structure. Cambridge University Press. CrossrefGoogle Scholar
Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E., & Dyer, C.
(2014) Metaphor detection with cross-lingual model transfer. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (pp. 248–258). The Association for Computer Linguistics.Google Scholar
van Genabith, J.
(1999) Metaphors and type theory. In Proceedings of the AISB’99 Symposium on Metaphor, Artificial Intelligence, and Cognition (pp. 108–112).
Veale, T., & Hao, Y.
(2007) Comprehending and generating apt metaphors: A web-driven, case-based approach to figurative language. AAAI , 1471–1476.
Wilks, Y.
(1978) Making preferences more active. Artificial Intelligence, 11(3), 197–223. CrossrefGoogle Scholar