Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
CbdA: Cloud Computing Based DMIS on ANEKA Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Identifying Metaphors Using Fuzzy Conceptual Features

Authors : Sunny Rai, Shampa Chakraverty, Devendra K. Tayal

Published in: Information, Communication and Computing Technology

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Metaphor comprehension is a challenging problem which equally intrigues researchers in linguistics as well as those working in the domain of cognition. The use of psychological features such as Imageability and Concreteness has been shown to be effective in identifying metaphors. However, there is a certain degree of vagueness and blurring boundaries between the sub-concepts of these features that has hitherto been largely ignored. In this paper, we tackle this issue of vagueness by proposing a fuzzy framework for metaphor detection whereby linguistic variables are employed to express psychological features. We develop a Mamdani Model to extract fuzzy classification rules for detecting metaphors in a text. The results of experiments conducted over a dataset of nominal metaphors reveal encouraging results with an F-score of more than 79%.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter An Optimal Multi-view Ensemble Learning for High Dimensional Data Classification Using Constrained Particle Swarm Optimization
Literature
1.
Bracewell, D.B., Tomlinson, M.T., Mohler, M., Rink, B.: A tiered approach to the recognition of metaphor. In: Computational Linguistics and Intelligent Text Processing, pp. 403–414. Springer, Heidelberg (2014) doi:10.​1007/​978-3-642-54906-9_​33
2.
Klebanov, B.B., Leong, C.W., Flor, M.: Supervised word-level metaphor detection: experiments with concreteness reweighting of examples. In: NAACL HLT 2015, p. 11 (2015)
3.
Thibodeau, P.H., Durgin, F.H.: Metaphor aptness and conventionality: a processing fluency account. Metaphor Symb. 26(3), 206–226 (2011)CrossRef
4.
Labov, W.: The boundaries of words and their meanings. In: Bailey, C.-J.N., Shuy, R.W. (eds.) New Ways of Analyzing Variation in English. Georgetown University Press, Washington, DC (1973)
5.
6.
Wilson, M.D.: The MRC psycholinguistic database: machine readable dictionary, version 2. Behav. Res. Methods Instrum. Comput. 20(1), 6–11 (1988)CrossRef
7.
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121(12), 1585–1588 (1974)CrossRef
8.
Riza, L.S., Bergmeir, C.N., Herrera, F., Benitez, J.M.: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R. American Statistical Association, Alexandria (2015)
9.
Wilks, Y.: Making preferences more active. Artif. Intell. 11(3), 197223 (1978)CrossRef
10.
Martin, J.H.: A Computational Model of Metaphor Interpretation. Academic Press Professional, Inc., Cambridge (1990)MATH
11.
Fass, D.: met*: a method for discriminating metonymy and metaphor by computer. Comput. Linguist. 17(1), 4990 (1991)
12.
Mason, Z.J.: CorMet: a computational, corpus-based conventional metaphor extraction system. Comput. Linguist. 30(1), 23–44 (2004)CrossRef
13.
Birke, J., Sarkar, A.: A clustering approach for the nearly unsupervised recognition of nonliteral language. In: Proceedings of EACL 2006, pp. 329–336 (2006)
14.
Gedigan, M., Bryant, J., Narayanan, S., Ciric, B.: Catching metaphors. In: Proceedings of the 3rd Workshop on Scalable Natural Language Understanding, New York, pp. 41–48 (2006)
15.
16.
Krishnakumaran, S., Zhu, X.: Hunting elusive metaphors using lexical resources. In: Proceedings of the Workshop on Computational Approaches to Figurative Language, Rochester, NY, pp. 13–20 (2007)
17.
Shutova, E., Sun, L., Korhonen, A.: Metaphor identification using verb and noun clustering. In: Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics (2010)
18.
Lakoff, G., Johnson, M.: Metaphors We Live by. University of Chicago Press, Chicago (1980)
19.
Turney, P.D., Neuman, Y., Assaf, D., Cohen, Y.: Literal and metaphorical sense identification through concrete and abstract context. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 680–690. Association for Computational Linguistics (July 2011)
20.
Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E., Dyer, C.: Metaphor Detection with Cross-Lingual Model Transfer. Language Technologies Institute, Carnegie Mellon University, Pittsburgh (2014)CrossRef
21.
Gargett, A., Barnden, J.: Modeling the interaction between sensory and affective meanings for detecting metaphor. In: NAACL HLT: 2015, p. 21 (2015)
22.
Rai, S., Chakraverty, S., Tayal, D.K.: Supervised metaphor detection using conditional random fields. In: Proceedings of the Fourth Workshop on Metaphor in NLP, NAACL-HLT 2016, pp. 18–27 (2016)
23.
Rentoumi, V., Vouros, G.A., Karkaletsis, V., Moser, A.: Investigating metaphorical language in sentiment analysis: a sense-to-sentiment perspective. ACM Trans. Speech Lang. Process. 9(3), 6 (2012)CrossRef
24.
Wang, L.-X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)MathSciNetCrossRef
25.
Bradley, M.M., Lang, P.J.: Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings, pp. 1–45. Technical report C-1. The Centre for Research in Psychophysiology, University of Florida (1999)
26.
Zeileis, A., Hornik, K., Smola, A., Karatzoglou, A.: Kernlab - an S4 package for kernel methods in R. J. Stat. Softw. 11(9), 1–20 (2004)
Metadata
Title
Identifying Metaphors Using Fuzzy Conceptual Features
Authors
Sunny Rai
Shampa Chakraverty
Devendra K. Tayal
Copyright Year
2017
Publisher
Springer Singapore
Book
Information, Communication and Computing Technology

Print ISBN: 978-981-10-6543-9

Electronic ISBN: 978-981-10-6544-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-6544-6_34

Premium Partner

    Image Credits