Detección de Metaforicidad en Pares Adjetivo-Sustantivo

  1. Andrés Torres Rivera
  2. Marta Coll-Florit
  3. Antoni Oliver
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2020

Issue: 64

Pages: 53-60

Type: Article

DIALNET GOOGLE SCHOLAR HANDLE: https://hdl.handle.net/10045/104727

More publications in: Procesamiento del lenguaje natural

Abstract

In this paper we propose a neural network approach to detect the metaphoricity of Adjective-Noun pairs using pre-trained word embeddings and word similarity using dot product. We found that metaphorical word pairs tend to have a lower dot product score while literal pairs a higher score. On this basis, we compared seven optimizers and two activation functions, from which the best performing pairs obtained an accuracy score of 97.69% and 97.74%, which represents an improvement of 6% over other current approaches.

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Funding information

This research was conducted in the frame-work of the project MOMENT: Metaphors of severe mental disorders. Discourse analysis of affected people and mental health professionals, funded by the Spanish National Research Agency (Agencia Estatal de Inves-tigación, AEI) and the European Regional Development Fund, within the National Programme for Research Aimed at the Challenges of Society. Ref. FFI2017-86969-R (AEI/FEDER, UE)

Funders

Bibliographic References

  • Bizzoni, Y., S. Chatzikyriakidis, and M. Ghanimifard. 2017. ”Deep” Learning : Detecting Metaphoricity in Adjective-Noun Pairs. In Proceedings of the Workshop on Stylistic Variation, pages 43–52, Copenhagen, Denmark. Association for Computational Linguistics.
  • Coll-Florit, M. and S. Climent. 2019. A new methodology for conceptual metaphor detection and formulation in corpora. a case study on a mental health corpus. SKY Journal of Linguistics, 32.
  • Gutierrez, E., E. Shutova, T. Marghetis, and B. Bergen. 2016. Literal and Metaphorical Senses in Compositional Distributional Semantic Models. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 183–193, Berlin, Germany. Association for Computational Linguistics.
  • Lakoff, G. and M. Johnson. 1980. Metaphors we Live by. University of Chicago Press, Chicago. Le, Q. V. and T. Mikolov. 2014. Distributed Representations of Sentences and Documents. arXiv:1405.4053 [cs], May. arXiv: 1405.4053.
  • Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013a. Efficient Estimation of Word Representations in Vector Space. CoRR, abs/1301.3.
  • Mikolov, T., I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. 2013b. Distributed Representations of Words and Phrases and their Compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, 2:9.
  • Mikolov, T., W. Yih, and G. Zweig. 2013. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746– 751. Association for Computational Linguistics. event-place: Atlanta, Georgia.
  • Mitchell, J. and M. Lapata. 2010. Composition in distributional models of semantics. Cognitive Science, 34(8):1388–1429.
  • Mu, J., H. Yannakoudakis, and E. Shutova. 2019. Learning Outside the Box: Discourse-level Features Improve Metaphor Identification. arXiv:1904.02246 [cs], April. arXiv: 1904.02246.
  • Pragglejaz Group. 2007. MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22(1):1– 39.
  • Rosen, Z. 2018. Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues. In Proceedings of the Workshop on Figurative Language Processing, pages 102–109, New Orleans, Louisiana. Association for Computational Linguistics.
  • Shutova, E. 2010. Models of Metaphor in NLP. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 688–697.
  • Shutova, E., 2017. Annotation of Linguistic and Conceptual Metaphor, pages 1073– 1100. Springer Netherlands, Dordrecht. Metaphoricity detection in adjective-noun pairs 59
  • Steen, G. J., A. G. Dorst, J. B. Herrmann, A. Kaal, T. Krennmayr, and T. Pasma. 2010. A Method for Linguistic Metaphor Identification: From MIP to MIPVU. John Benjamins.
  • Tsvetkov, Y., L. Boytsov, A. Gershman, E. Nyberg, and C. Dyer. 2014. Metaphor Detection with Cross-Lingual Model Transfer. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 248–258, Baltimore, Maryland. Association for Computational Linguistics.
  • Turney, P., Y. Neuman, D. Assaf, and Y. Cohen. 2011. Literal and Metaphorical Sense Identification through Concrete and Abstract Context. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 680– 690, Edinburgh, Scotland, UK., July. Association for Computational Linguistics.
  • Veale, T., E. Shutova, and B. B. Klebanov. 2016. Metaphor: A Computational Perspective. Synthesis Lectures on Human Language Technologies, 9(1):1– 160, February.
  • Wu, C., F. Wu, Y. Chen, S. Wu, Z. Yuan, and Y. Huang. 2018. Neural Metaphor Detecting with CNN-LSTM Model. In Proceedings of the Workshop on Figurative Language Processing, pages 110–114, New Orleans, Louisiana. Association for Computational Linguistics
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