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Articles

Vector Space Applications in Metaphor Comprehension

References

  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network(ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727.
  • Ahrens, K., Liu, H. L., Lee, C. Y., Gong, S. P., Fang, S. Y., & Hsu, Y. Y. (2007). FunctionalMRI of conventional and anomalous metaphors in Mandarin Chinese. Brain and Language, 100(2), 163–171. doi:10.1016/j.bandl.2005.10.004
  • Al-Azary, H., & Buchanan, L. (2017). Novel metaphor comprehension: Semantic neighbourhooddensity interacts with concreteness. Memory & Cognition, 45(2), 296–307.
  • Anderson, J. R., & Milson, R. (1989). Human memory: An adaptive perspective. Psychological Review, 96(4), 703–719. doi:10.1037/0033-295X.96.4.703
  • Aristotle. (1952). Rhetoric. In W. D. Ross (Ed.); W. R. Roberts (Trans.), The works of Aristotle Vol. 11: Theoretica, de rhetorica ad alexandrum, poetica (pp. 1–51). Oxford, UK: Clarendon Press.
  • Aristotle. (1996). Poetics. (M. Heath, trans.). London, UK: Penguin Books.
  • Bowdle, B. F., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112(1), 193–216. doi:10.1037/0033-295X.112.1.193
  • Clark, S. (2015). Vector space models of lexical meaning. In S. Lappin & C. Fox (Eds.), The Handbook of Contemporary Semantic Theory (2nd ed., pp. 493–522). Malden, MA: Wiley-Blackwell.
  • Dawson, M. R. W. (1998). Understanding cognitive science. Oxford, UK: Blackwell Publishers.
  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407. doi:10.1002/(ISSN)1097-4571
  • Durda, K., & Buchanan, L. (2008). WINDSORS: Windsor improved norms of distance and similarity of representations of semantics. Behavior Research Methods, 40(3), 705–712.
  • Eviatar, Z., & Just, M. A. (2006). Brain correlates of discourse processing: An fMRI investigation of irony and conventional metaphor comprehension. Neuropsychologia, 44(12), 2348–2359. doi:10.1016/j.neuropsychologia.2006.05.007
  • Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41(1), 1–63. doi:10.1016/0004-3702(89)90077-5
  • Fass, D. (1991). met*: A method for discriminating metonymy and metaphor by computer. Computational Linguistics, 17(1), 49–90.
  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. doi:10.1207/s15516709cog0702_3
  • Glucksberg, S. (2003). The psycholinguistics of metaphor. Trends in Cognitive Sciences, 7(2), 92–96.
  • Glucksberg, S., & Keysar, B. (1990). Understanding metaphorical comparisons: Beyond similarity. Psychological Review, 97(1), 3–18. doi:10.1037/0033-295X.97.1.3
  • Harman, D. (1986). An experimental study of the factors important in document ranking. In F. Rabitti (Ed.), Association for Computing Machinery 9th Conference on Research and Development in Information Retrieval (pp. 186–193). New York, NY: Association for Computing Machinery. doi:10.1145/253168.253206
  • Harris, Z. S. (1954). Distributional Structure. WORD, 10(2–3), 146–162. doi:10.1080/00437956.1954.11659520
  • Hofmann, T. (1999). Probabilistic latent semantic analysis. In K. B. Laskey & H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 289–296). San Francisco, CA: Morgan Kaufmann Publishers Inc. Retrieved from https://arxiv.org/ftp/arxiv/papers/1301/1301.6705.pdf
  • Jones, W. P. (1988). “As we may think”?: Psychological considerations in the design of a personal filing system. In R. Guindon (Ed.), Cognitive Science and its Applications for Human-Computer Interaction (pp. 235–287). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Kameya, Y., & Sato, T. (2005). Computation of probabilistic relationship between concepts and their attributes using a statistical analysis of Japanese corpora. In Proceedings of Symposium on Large-scale Knowledge Resources: LKR2005 (pp. 65–68), Tokyo, Japan. Retrieved from http://rjida.meijo-u.ac.jp/sato-www/reference/Kameya-LKR2005.pdf
  • Katz, A. N., & Al-Azary, H. (2017). Principles that promote bidirectionality in verbal metaphor. Poetics Today, 38(1), 35–59. doi:10.1215/03335372-3716215
  • Kintsch, W. (2000). Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 7(2), 257–266.
  • Kintsch, W. (2008). How the mind computes the meaning of metaphor: A simulation based on LSA. In R. W. Gibbs Jr. (Ed.), The Cambridge Handbook of Metaphor and Thought (pp. 129–142). New York, NY: Cambridge University Press.
  • Kintsch, W., & Bowles, A. R. (2002). Metaphor comprehension: What makes a metaphordifficult to understand? Metaphor and Symbol, 17(4), 249–262. doi:10.1207/S15327868MS1704_1
  • Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago, IL: University of Chicago Press.
  • Landauer, T. K., & Dumais, S. T. (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. doi:10.1037/0033-295X.104.2.211
  • Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2–3), 259–284. doi:10.1080/01638539809545028
  • Lemaire, B., & Bianco, M. (2003). Contextual effects on metaphor comprehension: Experiment and simulation. In F. Detje, D. Dörner, & H. Schaub (Eds.), Proceedings of the 5th International Conference on Cognitive Modeling (ICCM2003) (pp. 153–158). Bamberg, Germany: Universitäts‐Verlag Bamberg. Retrieved from http://cogprints.org/3205/1/iccm03_lemaire.pdf
  • Levy, O., & Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Proceedings of Advances in Neural Information Processing Systems (Vol. 27, pp. 2177–2185). Electronic proceedings, conference held in Montreal, Canada. Retrieved from http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
  • Lin, D. (1998). Automatic retrieval and clustering of similar words. In P. Isabelle (Ed.), Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (pp. 768–774). Stroudsburg, PA: Association for Computational Linguistics. doi:10.3115/980691.980696
  • Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co- occurrence. Behavior Research Methods, Instruments, & Computers, 28(2), 203–208. doi:10.3758/BF03204766
  • Marr, D. (1982). Vision: A computational investigation in the human representation of visual information. San Francisco, CA: Freeman.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. In Proceedings of International Conference on Learning Representations Workshop, Scottsdale, AZ. Retrieved from https://arxiv.org/abs/1301.3781
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Proceedings of Advances in Neural Information Processing Systems (Vol. 26, pp. 3111–3119). Electronic proceedings, conference held in Lake Tahoe, NV. Retrieved from https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
  • Mohler, M., Rink, B., Bracewell, D., & Tomlinson, M. (2014). A novel distributional approach to multilingual conceptual metaphor recognition. In J. Tsujii & J. Hajic (Eds.), Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (pp. 1752–1763). Dublin, Ireland: Association for Computational Linguistics. Retrieved from http://www.aclweb.org/anthology/C14-1165
  • Ortony, A. (1975). Why metaphors are necessary and not just nice. Educational Theory, 25(1), 45–53. doi:10.1111/edth.1975.25.issue-1
  • Ortony, A. (1979). Beyond literal similarity. Psychological Review, 86(3), 161–180. doi:10.1037/0033-295X.86.3.161
  • Padó, S., & Lapata, M. (2007). Dependency-based construction of semantic space models. Computational Linguistics, 33(2), 161–199. doi:10.1162/coli.2007.33.2.161
  • Rapp, A. M., Leube, D. T., Erb, M., Grodd, W., & Kircher, T. T. J. (2004). Neural correlates of metaphor processing. Cognitive Brain Research, 20(3), 395–402. doi:10.1016/j.cogbrainres.2004.03.017
  • Rubenstein, H., & Goodenough, J. B. (1965). Contextual correlates of synonymy. Communications of the ACM, 8(10), 627–633. doi:10.1145/365628.365657
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back- propagating errors. Nature, 323, 533–536. doi:10.1038/323533a0
  • Sahlgren, M. (2008). The distributional hypothesis. Italian Journal of Linguistics, 20(1), 33–54.
  • Shutova, E., Van de Cruys, T., & Korhonen, A. (2012). Unsupervised metaphor paraphrasing using a vector space model. In Proceedings of COLING 2012: Posters (pp. 1121–1130), Mumbai, India. Retrieved from http://www.aclweb.org/anthology/C12-2109
  • Su, C., Huang, S., & Chen, Y. (2017). Automatic detection and interpretation of nominal metaphor based on the theory of meaning. Neurocomputing, 219(5), 300–311. doi:10.1016/j.neucom.2016.09.030
  • Terai, A., & Nakagawa, M. (2012). A corpus-based computational model of metaphor understanding consisting of two processes. Cognitive Systems Research, 19, 30–38. doi:10.1016/j.cogsys.2012.03.001
  • Tourangeau, R., & Sternberg, R. J. (1981). Aptness in metaphor. Cognitive Psychology, 13(1), 27–55. doi:10.1016/0010-0285(81)90003-7
  • Touretzky, D. S., & Pomerleau, D. A. (1989). What’s hidden in the hidden layers?. Byte, 14(8), 227–233.
  • Trick, L., & Katz, A. N. (1986). The domain interaction approach to metaphor processing: Relating individual differences and metaphor characteristics. Metaphor and Symbolic Activity, 1(3), 185–213. doi:10.1207/s15327868ms0103_3
  • Utsumi, A. (2005). The role of feature emergence in metaphor appreciation. Metaphor and Symbol, 20(3), 151–172. doi:10.1207/s15327868ms2003_1
  • Utsumi, A. (2011). Computational exploration of metaphor comprehension processes using a semantic space model. Cognitive Science, 35(2), 251–296. doi:10.1111/j.1551-6709.2010.01144.x
  • Van de Cruys, T., Poibeau, T., & Korhonen, A. (2011). Latent vector weighting for word meaning in context. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (pp. 1476–1485). Stroudsburg, PA: Association for Computational Linguistics. Retrieved from https://hal.archives-ouvertes.fr/hal-00666475/document

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