References
Almuhareb, A., & Poesio, M. (2004). Attribute-based and value-based clustering: An evaluation. In
Proceedings of EMNLP 2004
. Barcelona.
Almuhareb, A., & Poesio, M. (2005). Concept learning and categorization from the web. In
Proceedings of the 27th annual meeting of the Cognitive Science Society
.
Barnden, J. A. (2006). Artificial intelligence, figurative language and cognitive linguistics. In G. Kristiansen, M. Achard, R. Dirven, & F. J. Ruiz de Mendoza Ibáñez (Eds.), Cognitive linguistics: Current application and future perspectives (pp. 431–459). Berlin: Mouton de Gruyter.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Brants, T., & Franz, A. (2006). Web 1T 5-gram Ver. 1. Linguistic Data Consortium.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Brennan, S. E., & Clark, H. H. (1996). Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory and Cognition, 22(6), 1482–1493. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Fass, D. (1991). Met*: A method for discriminating metonymy and metaphor by computer. Computational Linguistics, 17(1), 9–90.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Fass, D. (1997). Processing metonymy and metaphor. Contemporary studies in cognitive science & technology. New York: Ablex.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Fishelov, D. (1992). Poetic and non-poetic simile: Structure, semantics, rhetoric. Poetics Today, 14(1), 1–23. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Gibbs, R. W., Jr. (2015). Does deliberate metaphor theory have a future? Journal of Pragmatics, 90, 73–76. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Glucksberg, S., & Keysar, B. (1990). Understanding metaphorical comparisons: Beyond similarity. Psychological Review, 97(1), 3–18. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Hanks, P. (2005). Similes and sets: The English preposition ‘like’. In Blatná, R., & Petkevic, V. (Eds.), Languages and linguistics: Festschrift for Fr. Cermak. Prague: Charles University.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Hanks, P. (2006). Metaphoricity is gradable. In A. Stefanowitsch, & S. Th. Gries (Eds.), Corpus-based approaches to metaphor and metonymy (pp. 17–35). Berlin: Mouton de Gruyter.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. In
Proceedings of the 14th international conference on computational linguistics
(pp. 539–545). ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: Cambridge University Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Kintsch, W. (2000). Metaphor comprehension: A computational theory. Psychonomic Bulletin Review, 7(2), 257–266. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago: The University of Chicago Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago: The University of Chicago Press. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Martin, J. H. (1990). A computational model of metaphor interpretation. New York: Academic Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Mason, Z. J. (2004). CorMet: A computational, corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Mihalcea, R. (2002). The semantic wildcard. In
Proceedings of the LREC workshop on creating and using semantics for information retrieval and filtering
. Canary Islands, Spain, May 2002.
Navigli, R., & Velardi, P. (2003). An analysis of ontology-based query expansion strategies. In
Proceedings of the workshop on adaptive text extraction and mining (ATEM 2003), at ECML 2003, the 14th European conference on machine learning
(pp. 42–49).
Pasca, M., & Van Durme, B. (2007). What you seek is what you get: Extraction of class attributes from query logs. In
Proceedings of the 20th international joint conference on artifical intelligence
(pp. 2832–2837).
Salton, G. (1968). Automatic information organization and retrieval. New York: McGraw-Hill.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Shutova, E. (2010). Metaphor identification using verb and noun clustering. In
Proceedings of the 23rd international conference on computational linguistics
(pp. 1001–1010).
Steen, G. J. (2015). Developing, testing and interpreting deliberate metaphor theory. Journal of Pragmatics, 90, 67–72. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
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. Amsterdam: John Benjamins Publishing Company. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Taylor, A. (1954). Proverbial comparisons and similes from California. Folklore Studies 3. Berkeley: University of California Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Turney, P. D., & Littman, M. L. (2005). Corpus-based learning of analogies and semantic relations. Machine Learning 60(1–3), 251–278. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Van Rijsbergen, C. J. (1979). Information retrieval. Oxford: Butterworth-Heinemann.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Veale, T. (2004). The challenge of creative information retrieval. Computational Linguistics and Intelligent Text Processing: Lecture Notes in Computer Science, 2945/2004, 457–467. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Veale, T. (2011). Creative language retrieval: A robust hybrid of information retrieval and linguistic creativity. In
Proceedings of ACL’2011, the 49th annual meeting of the association of computational linguistics
. June 2011.
Veale, T. (2015). Unnatural selection: Seeing human intelligence in artificial creations. Journal of General Artificial Intelligence, 6(1), 5–20. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Veale, T., & Hao, Y. (2007a). Making lexical ontologies functional and context-sensitive. In
Proceedings of the 46th annual meeting of the Assocciation of Computational Linguistics
.
Veale, T., & Hao, Y. (2007b). Comprehending and generating apt metaphors: A web-driven, case-based approach to figurative language. In
Proceedings of AAAI 2007, the 22nd AAAI conference on artificial intelligence
. Vancouver, Canada.
Veale, T., & Hao, Y. (2008). Talking points in metaphor: A concise, usage-based representation for figurative processing. In
Proceedings of ECAI’2008, the 18th European conference on artificial intelligence
. Patras, Greece, July 2008.
Veale, T., & Hao, Y. (2012). In the mood for affective search with web stereotypes. In
Proceedings of the 21st international conference on World Wide Web
, Lyon, France. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
Veale, T., Shutova, E., & Beigman Klebanov, B. (2016). Metaphor: A computational perspective. Morgan Claypool, Synthesis Lectures on Human Language Technologies. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Vernimb, C. (1977). Automatic query adjustment in document retrieval. Information Processing & Management, 13(6), 339–353. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Voorhees, E. M. (1994). Query expansion using lexical-semantic relations. In Proceedings of SIGIR 94, the 17th international conference on research and development in information retrieval (pp. 61–69). Berlin: Springer. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Voorhees, E. M. (1998). Using WordNet for text retrieval. In WordNet, an electronic lexical database (pp. 285–303). Cambridge, MA: The MIT Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Wilks, Y. (1978). Making preferences more active, Artificial Intelligence, 11(3), 197–223. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Xu, J., & Croft, B. W. (1996). Query expansion using local and global document analysis. In
Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval
. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)