Skip to main content

Determining the Conceptual Space of Metaphoric Expressions

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7816))

Abstract

We present a method of constructing the semantic signatures of target concepts expressed in metaphoric expressions as well as a method to determine the conceptual space of a metaphor using the constructed semantic signatures and a semantic expansion. We evaluate our methodology by focusing on metaphors where the target concept is Governance. Using the semantic signature constructed for this concept, we show that the conceptual spaces generated by our method are judged to be highly acceptable by humans.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahrens, K., Chung, S., Huang, C.: Conceptual metaphors: Ontology-based representation and corpora driven mapping principles. In: Proceedings of the ACL 2003 Workshop on Lexicon and Figurative Language, vol. 14, pp. 36–42. Association for Computational Linguistics (2003)

    Google Scholar 

  2. Wilks, Y.: Making preferences more active. Artificial Intelligence 11(3), 197–223 (1978)

    Article  Google Scholar 

  3. Lakoff, G., Johnson, M.: Metaphors we live by, Chicago, London, vol. 111 (1980)

    Google Scholar 

  4. Tourangeau, R., Sternberg, R.: Understanding and appreciating metaphors. Cognition 11(3), 203–244 (1982)

    Article  Google Scholar 

  5. Shutova, E.: Models of metaphor in nlp. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 688–697. Association for Computational Linguistics (2010)

    Google Scholar 

  6. Lakoff, G., et al.: The contemporary theory of metaphor. Metaphor and Thought 2, 202–251 (1993)

    Article  Google Scholar 

  7. Shutova, E., Teufel, S.: Metaphor corpus annotated for source-target domain mappings. In: Proceedings of LREC (2010)

    Google Scholar 

  8. Fass, D.: met*: a method for discriminating metonymy and metaphor by computer. Comput. Linguist. 17(1), 49–90 (1991)

    Google Scholar 

  9. Shutova, E., Sun, L., Korhonen, A.: Metaphor identification using verb and noun clustering. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 1002–1010. Association for Computational Linguistics, Stroudsburg (2010)

    Google Scholar 

  10. Mason, Z.J.: Cormet: a computational, corpus-based conventional metaphor extraction system. Comput. Linguist. 30(1), 23–44 (2004)

    Article  Google Scholar 

  11. Wolff, P., Gentner, D.: Evidence for role-neutral initial processing of metaphors. Journal of Experimental Psychology: Learning, Memory, and Cognition 26(2), 529 (2000)

    Article  Google Scholar 

  12. McGlone, M.: Conceptual metaphors and figurative language interpretation: Food for thought? Journal of Memory and Language 35(4), 544–565 (1996)

    Article  Google Scholar 

  13. Lakoff, G.: Master Metaphor List. University of California (1994)

    Google Scholar 

  14. Eilts, C., Lönneker, B.: The hamburg metaphor database (2002)

    Google Scholar 

  15. Bogdanova, D.: A framework for figurative language detection based on sense differentiation. In: Proceedings of the ACL 2010 Student Research Workshop. ACLstudent 2010, pp. 67–72 (2010)

    Google Scholar 

  16. Li, L., Sporleder, C.: Using gaussian mixture models to detect figurative language in context. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 297–300 (2010)

    Google Scholar 

  17. Peters, W., Wilks, Y.: Data-driven detection of figurative language use in electronic language resources. Metaphor and Symbol 18(3), 161–173 (2003)

    Article  Google Scholar 

  18. Shutova, E.: Computational approaches to figurative language. PhD thesis, University of Cambridge (2011)

    Google Scholar 

  19. Martin, J.: A computational model of metaphor interpretation. Academic Press Professional, Inc. (1990)

    Google Scholar 

  20. Feldman, J., Narayanan, S.: Embodied meaning in a neural theory of language. Brain and Language 89(2), 385–392 (2004)

    Article  Google Scholar 

  21. Barnden, J., Glasbey, S., Lee, M., Wallington, A.: Reasoning in metaphor understanding: The att-meta approach and system. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 2, pp. 1–5. Association for Computational Linguistics (2002)

    Google Scholar 

  22. Lin, C.Y., Hovy, E.: The automated acquisition of topic signatures for text summarization. In: Proceedings of the 18th Conference on Computational Linguistics, COLING 2000, vol. 1, pp. 495–501 (2000)

    Google Scholar 

  23. Harabagiu, S., Lacatusu, F.: Topic themes for multi-document summarization. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 202–209. ACM (2005)

    Google Scholar 

  24. Lönneker, B.: Is there a way to represent metaphors in wordnets?: insights from the hamburg metaphor database. In: Proceedings of the ACL 2003 Workshop on Lexicon and Figurative Language, LexFig 2003, vol. 14, pp. 18–27 (2003)

    Google Scholar 

  25. Pease, A., Niles, I., Li, J.: The suggested upper merged ontology: A large ontology for the semantic web and its applications. In: Working Notes of the AAAI 2002 Workshop on Ontologies and the Semantic Web, Edmonton, Canada, vol. 28 (2002)

    Google Scholar 

  26. Fellbaum, C.: Wordnet: An electronic lexical database (1998), WordNet is available from http://www.cogsci.princeton.edu/wn

  27. Balkova, V., Sukhonogov, A., Yablonsky, S.: Russian wordnet. from uml-notation to internet/intranet database implementation. In: Proceedings of the Second International WordNet Conference (2004)

    Google Scholar 

  28. Atserias, J., Villarejo, L., Rigau, G., Agirre, E., Carroll, J., Magnini, B., Vossen, P.: The MEANING Multilingual Central Repository. In: Proceedings of the 2nd Global WordNet Conference (GWC), Brno, Czech Republic (January 2004)

    Google Scholar 

  29. Toral, A., Ferrández, O., Agirre, E., Munoz, R., Fakultatea, I., Donostia, B.: A study on linking wikipedia categories to wordnet synsets using text similarity. In: Proceedings of the 7th International Conference on Recent Advances in Natural Language Processing, pp. 449–454 (2009)

    Google Scholar 

  30. Niemann, E., Gurevych, I.: The people’s web meets linguistic knowledge: automatic sense alignment of wikipedia and wordnet. In: Proceedings of the Ninth International Conference on Computational Semantics, IWCS 2011, pp. 205–214 (2011)

    Google Scholar 

  31. Heydon, A., Najork, M.: Mercator: A scalable, extensible web crawler. World Wide Web 2(4), 219–229 (1999)

    Article  Google Scholar 

  32. Bracewell, D.B., Ren, F., Kuroiwa, S.: Mining News Sites to Create Special Domain News Collections. Computational Intelligence 4(1), 56–63 (2007)

    Google Scholar 

  33. Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: An Electronic Lexical Database 305, 305–332 (1998)

    Google Scholar 

  34. Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 73–80. Association for Computational Linguistics (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bracewell, D.B., Tomlinson, M.T., Mohler, M. (2013). Determining the Conceptual Space of Metaphoric Expressions. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37247-6_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37247-6_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37246-9

  • Online ISBN: 978-3-642-37247-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics