Berber Sardinha, Tony
(2008) Metaphor probabilities in corpora. In M. S. Zanotto, L. Cameron, & M. C. Cavalcanti (Eds.), Confronting metaphor in use: An applied linguistic approach (pp. 127–147). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Berber Sardinha, T.
(2012) An assessment of metaphor retrieval methods. In F. MacArthur, J. L. Oncins-Martínez, M. Sánchez-García, & A. M. Piquer Píriz (Eds.), Metaphor in use: Context, culture, and communication (pp. 21–50). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Cameron, L., & Deignan, A.
(2003) Combining large and small corpora to investigate tuning devices around metaphor in spoken discourse. Metaphor and Symbol 18 (3): 149–160. DOI logoGoogle Scholar
Colston, H. L.
(2015) Using Figurative Language. New York: Cambridge University Press. DOI logoGoogle Scholar
Coulson, S., & Matlock, T.
(2001) Metaphor and the space structuring model. Metaphor and Symbol, 16(3–4): 295–316. DOI logoGoogle Scholar
Cruse, D. A.
(2001) The lexicon. In M. Aronoff, & J. Rees-Millier (Eds.), The Handbook of Linguistics (pp. 238–264). Oxford: Blackwell.Google Scholar
Dancygier, B., & Sweetser, E.
(2014) Figurative Language. Cambridge: Cambridge University Press.Google Scholar
David, O., Lakoff, G., & Stickles, E.
(2016) Cascades in metaphor and grammar: A case study of metaphors in the gun debate. Constructions and Frames, 8(2): 214–253. DOI logoGoogle Scholar
Dodge, E., Hong, J., & Stickles, E.
(2015) MetaNet: Deep semantic automatic metaphor analysis. Third Workshop on Metaphor in NLP 2015. Denver, Colorado, USA, 5 June 2015: 40–49.Google Scholar
Gabrilovich, E., & Markovitch, S.
(2007) Computing semantic relatedness using wikipedia-based explicit semantic analysis. Proceedings of the International Joint Conference on Artificial Intelligence, 1606–1611.Google Scholar
Gibbs, R. W., Jr & Colston, H. L.
(2012) Interpreting Figurative Meaning. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Goatly, A.
(1997) The Language of metaphors. London: Routledge. DOI logoGoogle Scholar
Handl, S.
(2011) The Conventionality of figurative language: A usage-based study. Tübingen: Narr Francke Attempto Verlag.Google Scholar
Kövecses, Z.
(2000) The scope of metaphor. In A. Barcelona (Ed.), Metaphor and metonymy at the crossroads: A cognitive perspective (79–92). Berlin: Mouton de Gruyter.Google Scholar
(2015) Two ways of studying emotion metaphors in cognitive linguistics. Paper presented at the workshop Emotion Concepts in Use , June 25–26, 2015, Heinrich-Heine-University, Düsseldorf.
Leong, C., Beigman Klebanov, B. and Shutova, E.
(2018) A Report on the 2018 VUA Metaphor Detection Shared Task. Proceedings of the Workshop on Figurative Language Processing. Association for Computational Linguistics.Google Scholar
Li, H., Zhu, K. Q., & Wang, H.
(2013) Data-driven metaphor recognition and explanation. Transactions of the Association for Computational Linguistics, 1, 379–390. DOI logoGoogle Scholar
Markert, K., & Nissim, M.
(2006) Metonymic proper names: A corpus-based account. In A. Stefanowitsch, & S.Th. Gries (Eds.), Corpus-based approaches to metaphor and metonymy (pp. 152–174). Berlin: Mouton de Gruyter.Google Scholar
Nissim, M., & Markert, K.
(2003) Syntactic features and word similarity for supervised metonymy resolution. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL2003).Google Scholar
Parasuraman, A., Grewal, D., & Krishnan, R.
(2004) Marketing research. Boston: Houghton Mifflin.Google Scholar
Peirsman, Y.
(2006) What’s in a name? The automatic recognition of metonymical location names. Proceedings of the EACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together (pp. 25–32). Trento: ACL.Google Scholar
Shutova, E.
(2009) Sense-based interpretation of logical metonymy using a statistical method. Proceedings of the ACL-IJCNLP 2009 Student Research Workshop, Singapore, 1–9. DOI logoGoogle Scholar
Shutova, E., Kaplan, J., Teufel, S., & Korhonen, A.
(2013) A computational model of logical metonymy. ACM Transactions on Speech and Language Processing, 10(3), 1–28. DOI logoGoogle Scholar
Shutova, E., & Sun, L.
(2013) Unsupervised metaphor identification using hierarchical graph factorization clustering. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2013, 978–988.Google Scholar
Shutova, E., Sun, L. & Korhonen, A.
(2010) Metaphor Identification Using Verb and Noun Clustering. In Proceedings of COLING 2010, Beijing: China.Google Scholar
Shutova, E., Teufel, S., & Korhonen, A.
(2013) Statistical metaphor processing. Computational Linguistics, 39(2), 301–353. DOI logoGoogle Scholar
Stefanowitsch, A.
(2004)  happiness in English and German: A metaphorical-pattern analysis. In M. Achard, & S. Kemmer (Eds.), Language, culture, and mind (pp. 137–149). Stanford, Calif.: CSLI Publications.Google Scholar
(2006) Words and their metaphors: A corpus-based approach. In A. Stefanowitsch, & S. Th. Gries (Eds.), Corpus-based approaches to metaphor and metonymy (pp. 63–105). Berlin: Mouton de Gruyter.Google Scholar
Steen, G. J.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A., Krennmayr, T., & Pasma, T.
(2010) A Method for Linguistic Metaphor Identification: From MIP to MIPVU. Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Wallington, A. M., Barnden, J. A., Barnden, M. A., Ferguson, F. J., & Glaseby, S. R.
(2003) Metaphoricity signals: A corpus-based investigation. Birmingham: School of Computer Science, University of Birmingham, U.K.Google Scholar
Winston, M. E., Chaffin, R., & Herrmann, D.
(1987) A taxonomy of part–whole relations. Cognitive Science, 11(4), 417–444. DOI logoGoogle Scholar
Cited by

Cited by 3 other publications

Brglez, Mojca, Omnia Zayed & Paul Buitelaar
2024. TCMeta: a multilingual dataset of COVID tweets for relation-level metaphor analysis. Language Resources and Evaluation DOI logo
Broccias, Cristiano
2022. A Cognitive Grammar approach to ‘metonymy’. In Figurative Thought and Language in Action [Figurative Thought and Language, 16],  pp. 37 ff. DOI logo
Farkhani, Sadaf, Søren Kelstrup Skovsen, Mads Dyrmann, Rasmus Nyholm Jørgensen & Henrik Karstoft
2021. Weed Classification Using Explainable Multi-Resolution Slot Attention. Sensors 21:20  pp. 6705 ff. DOI logo

This list is based on CrossRef data as of 24 may 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.