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A survey on computational metaphor processing techniques: from identification, interpretation, generation to application

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Abstract

Metaphors are figurative expressions frequently appearing daily. Given its significance in downstream natural language processing tasks such as machine translation and sentiment analysis, computational metaphor processing has led to an upsurge in the community. The progress of Artificial Intelligence has incentivized several technological tools and frameworks in this domain. This article aims to comprehensively summarize and categorize previous computational metaphor processing approaches regarding metaphor identification, interpretation, generation, and application. Though studies on metaphor identification have made significant progress, metaphor understanding, conceptual metaphor processing, and metaphor generation still need in-depth analysis. We hope to identify future directions for prospective researchers based on comparing the strengths and weaknesses of the previous works.

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Notes

  1. Italics are metaphors.

  2. http://www.natcorp.ox.ac.uk/.

  3. http://www.vismet.org/metcor/documentation/home.html.

  4. https://github.com/ytsvetko/metaphor.

  5. http://trac.sketchengine.co.uk/wiki/Corpora/enTenTen.

  6. http://saifmohammad.com/WebPages/metaphor.html.

  7. www.crowdflower.com.

  8. http://bit.ly/1TQ5czN.

  9. https://catalog.ldc.upenn.edu/LDC2014T06.

  10. https://github.com/EducationalTestingService/metaphor.

  11. https://github.com/liaolianfoka/MET-Meme-A-Multi-modal-Meme-Dataset-Rich-in-Metaphors.

  12. https://github.com/yuri-bizzoni/Metaphor-Paraphrase.

  13. https://github.com/nightingal3/Fig-QA.

  14. http://www.lemurproject.org/clueweb09.php/.

  15. http://www.debatepolitics.com/.

  16. http://corpus.leeds.ac.uk/tools/ru/ruwac-parsed.out.xz.

  17. http://ece.ut.ac.ir/dbrg/hamshahri/.

  18. http://www.languagecomputer.com/metaphor-data.html.

  19. https://github.com/tuhinjubcse/MetaphorGenNAACL2021.

  20. https://github.com/aparrish/gutenberg-poetry-corpus.

  21. https://osf.io/7emr6/.

  22. Supersenses are called “lexicographer classes” in WordNet documentation.

  23. A feed-forward neural network is a network where connections between nodes do not form a cycle, which differs from a recurrent neural network.

  24. “Universal” means the models are not metaphor-specific and can be applied in other linguistic tasks, such as sequence labeling or classification tasks.

  25. http://nlp.stanford.edu/data/WestburyLab.wikicorp.201004.txt.bz2.

  26. https://www.lexico.com/.

  27. https://www.merriam-webster.com/dictionary/.

  28. https://dict.baidu.com.

  29. A database by NLP Lab of Xiamen University.

  30. An adjective taxonomy database from https://afflatus.ucd.ie/.

  31. A Chinese Thesaurus, http://ir.hit.edu.cn/.

  32. https://framenet.icsi.berkeley.edu.

  33. https://www.wiktionary.org.

  34. https://metapro.ruimao.tech/ Accessed 1st October 2022.

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Acknowledgements

This study is supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

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  1. Continental-NTU Corporate Lab, Nanyang Technological University, Singapore, Singapore

    Mengshi Ge, Rui Mao & Erik Cambria

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  1. Mengshi Ge
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  2. Rui Mao
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  3. Erik Cambria
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MG and RM wrote the main manuscript and Erik Cambria reviewed the manuscript.

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Correspondence to Erik Cambria.

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Ge, M., Mao, R. & Cambria, E. A survey on computational metaphor processing techniques: from identification, interpretation, generation to application. Artif Intell Rev 56 (Suppl 2), 1829–1895 (2023). https://doi.org/10.1007/s10462-023-10564-7

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