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Automatic metaphor interpretation as a paraphrasing task

Author:

Ekaterina ShutovaAuthors Info & Claims

HLT '10: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Pages 1029 - 1037

Published: 02 June 2010 Publication History

Abstract

We present a novel approach to metaphor interpretation and a system that produces literal paraphrases for metaphorical expressions. Such a representation is directly transferable to other applications that can benefit from a metaphor processing component. Our method is distinguished from the previous work in that it does not rely on any hand-crafted knowledge about metaphor, but in contrast employs automatically induced selectional preferences. Being the first of its kind, our system is capable of paraphrasing metaphorical expressions with a high accuracy (0.81).

References

[1]

R. Agerri, J. A. Barnden, M. G. Lee, and A. M. Wallington. 2007. Metaphor, inference and domain-independent mappings. In Proceedings of International Conference on Recent Advances in Natural Language Processing (RANLP-2007), pages 17--23, Borovets, Bulgaria.

[2]

O. E. Andersen, J. Nioche, E. Briscoe, and J. Carroll. 2008. The BNC parsed with RASP4UIMA. In Proceedings of the Sixth International Language Resources and Evaluation Conference (LREC'08), Marrakech, Morocco.

[3]

J. A. Barnden and M. G. Lee. 2002. An artificial intelligence approach to metaphor understanding. Theoria et Historia Scientiarum, 6(1):399--412.

[4]

E. Briscoe, J. Carroll, and R. Watson. 2006. The second release of the rasp system. In Proceedings of the COLING/ACL on Interactive presentation sessions, pages 77--80.

Digital Library

[5]

L. Burnard. 2007. Reference Guide for the British National Corpus (XML Edition). URL=http://www.natcorp.ox.ac.uk/XMLedition/URG/.

[6]

D. Fass. 1991. met*: A method for discriminating metonymy and metaphor by computer. Computational Linguistics, 17(1):49--90.

Digital Library

[7]

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

[8]

C. Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database (ISBN: 0-262-06197-X). MIT Press, first edition.

[9]

D. Hofstadter and M. Mitchell. 1994. The Copycat Project: A model of mental fluidity and analogy-making. In K. J. Holyoak and J. A. Barnden, editors, Advances in Connectionist and Neural Computation Theory, Ablex, New Jersey.

[10]

D. Hofstadter. 1995. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. HarperCollins Publishers.

Digital Library

[11]

G. Lakoff and M. Johnson. 1980. Metaphors We Live By. University of Chicago Press, Chicago.

[12]

J. H. Martin. 1988. Representing regularities in the metaphoric lexicon. In Proceedings of the 12th conference on Computational linguistics, pages 396--401.

Digital Library

[13]

J. H. Martin. 1990. A Computational Model of Metaphor Interpretation. Academic Press Professional, Inc., San Diego, CA, USA.

Digital Library

[14]

S. Narayanan. 1997. Knowledge-based action representations for metaphor and aspect (KARMA). Technical report, PhD thesis, University of California at Berkeley.

Digital Library

[15]

S. Narayanan. 1999. Moving right along: A computational model of metaphoric reasoning about events. In In Proceedings of the National Conference on Artificial Intelligence (AAAI 99), pages 121--128, Orlando, Florida.

Digital Library

[16]

Pragglejaz Group (P. Crisp, R. Gibbs, A. Cienki, G. Low, G. Steen, L. Cameron, E. Semino, J. Grady, A. Deignan and Z. Kovecses). 2007. MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22:1--39.

[17]

P. Resnik. 1993. Selection and Information: A Class-based Approach to Lexical Relationships. Ph.D. thesis, Philadelphia, PA, USA.

Digital Library

[18]

P. Resnik. 1997. Selectional preference and sense disambiguation. In ACL SIGLEX Workshop on Tagging Text with Lexical Semantics, Washington, D.C.

[19]

S. Siegel and N. J. Castellan. 1988. Nonparametric statistics for the behavioral sciences. McGraw-Hill Book Company, New York, USA.

[20]

L. Sun and A. Korhonen. 2009. Improving verb clustering with automatically acquired selectional preferences. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 638--647, Singapore, August.

Digital Library

[21]

T. Veale and Y. Hao. 2008. A fluid knowledge representation for understanding and generating creative metaphors. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 945--952, Manchester, UK.

Digital Library

[22]

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

Cited By

Lai HNissim M(2024)A Survey on Automatic Generation of Figurative Language: From Rule-based Systems to Large Language ModelsACM Computing Surveys10.1145/365479556:10(1-34)Online publication date: 30-Mar-2024
https://dl.acm.org/doi/10.1145/3654795
Su CHuang SChen Y(2017)Automatic detection and interpretation of nominal metaphor based on the theory of meaningNeurocomputing10.1016/j.neucom.2016.09.030219:C(300-311)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.030
Marton Y(2013)Distributional phrasal paraphrase generation for statistical machine translationACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836724:3(1-32)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483672
Show More Cited By

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HLT '10: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

June 2010

1070 pages

ISBN:1932432655

General Chair:
Ronald M. Kaplan
Microsoft Bing

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 02 June 2010

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Overall Acceptance Rate 240 of 768 submissions, 31%

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Cited By

Lai HNissim M(2024)A Survey on Automatic Generation of Figurative Language: From Rule-based Systems to Large Language ModelsACM Computing Surveys10.1145/365479556:10(1-34)Online publication date: 30-Mar-2024
https://dl.acm.org/doi/10.1145/3654795
Su CHuang SChen Y(2017)Automatic detection and interpretation of nominal metaphor based on the theory of meaningNeurocomputing10.1016/j.neucom.2016.09.030219:C(300-311)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.030
Marton Y(2013)Distributional phrasal paraphrase generation for statistical machine translationACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836724:3(1-32)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483672
Séaghdha DKorhonen AAgirre E(2012)Modelling selectional preferences in a lexical hierarchyProceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation10.5555/2387636.2387665(170-179)Online publication date: 7-Jun-2012
https://dl.acm.org/doi/10.5555/2387636.2387665
Kiddon CBrun YLin D(2011)That's what she saidProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 210.5555/2002736.2002756(89-94)Online publication date: 19-Jun-2011
https://dl.acm.org/doi/10.5555/2002736.2002756
Shutova ESun LKorhonen AJoshi AHuang CJurafsky D(2010)Metaphor identification using verb and noun clusteringProceedings of the 23rd International Conference on Computational Linguistics10.5555/1873781.1873894(1002-1010)Online publication date: 23-Aug-2010
https://dl.acm.org/doi/10.5555/1873781.1873894
Shutova EHajič J(2010)Models of metaphor in NLPProceedings of the 48th Annual Meeting of the Association for Computational Linguistics10.5555/1858681.1858752(688-697)Online publication date: 11-Jul-2010
https://dl.acm.org/doi/10.5555/1858681.1858752

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