More Web Proxy on the site http://driver.im/

research-article

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

Authors:

Erik CambriaAuthors Info & Claims

Artificial Intelligence Review, Volume 56, Issue Suppl 2

Pages 1829 - 1895

https://doi.org/10.1007/s10462-023-10564-7

Published: 11 August 2023 Publication History

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.

References

[1]

Agirre E, Stevenson M (2007) Knowledge sources for WSD. In: Word sense disambiguation. Springer, pp 217–251

[2]

Baker CF, Fillmore CJ, Lowe JB (1998) The Berkeley FrameNet project. In: The 17th international conference on computational linguistics (COLING)

[3]

Barcelona A et al. Metaphor and metonymy at the crossroads 2000 New York De Gruyter Mouton

[4]

Baroni M, Bernardini S, Ferraresi A, et al. The WaCky wide web: a collection of very large linguistically processed web-crawled corpora Lang Resour Eval 2009 43 3 209-226

[5]

Barsalou LW (2019) Flexibility, structure, and linguistic vagary in concepts: manifestations of a compositional system of perceptual symbols. In: Theories of memory. Psychology Press, pp 29–101

[6]

Bergsma S, Lin D, Goebel R (2008) Discriminative learning of selectional preference from unlabeled text. In: Proceedings of the 2008 conference on empirical methods in natural language processing, pp 59–68

[7]

Bickerton D Prolegomena to a linguistic theory of metaphor Found Lang 1969 14 34-52

[8]

Billow RM A cognitive developmental study of metaphor comprehension Dev Psychol 1975 11 4 415

[9]

Birke J, Sarkar A (2006) A clustering approach for nearly unsupervised recognition of nonliteral language. In: 11th Conference of the European chapter of the association for computational linguistics, pp 329–336

[10]

Bizzoni Y, Lappin S (2018) Predicting human metaphor paraphrase judgments with deep neural networks. In: Proceedings of the workshop on figurative language processing, pp 45–55

[11]

Borghi AM and Zarcone E Grounding abstractness: abstract concepts and the activation of the mouth Front Psychol 2016 7 1498

[12]

Bosselut A, Rashkin H, Sap M, et al (2019) COMET: Commonsense transformers for automatic knowledge graph construction. arXiv preprint arXiv:1906.05317

[13]

Brants T, Franz A (2006) Web 1T 5-gram Version 1. Linguistic data consortium. Philadelphia LDC2006T13

[14]

Brinton DM, Brinton LJ (2010) The linguistic structure of modern English. pp 1–446

[15]

Broadwell GA, Boz U, Cases I, et al (2013) Using imageability and topic chaining to locate metaphors in linguistic corpora. In: International conference on social computing, behavioral-cultural modeling, and prediction, Springer, pp 102–110

[16]

Brooks J, Youssef A (2020) Discriminative pattern mining for natural language metaphor generation. In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp 4276–4283

[17]

Brysbaert M, Warriner AB, and Kuperman V Concreteness ratings for 40 thousand generally known English word lemmas Behav Res Methods 2014 46 3 904-911

[18]

Bulat L, Clark S, Shutova E (2017) Modelling metaphor with attribute-based semantics. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 2, Short Papers, pp 523–528

[19]

Burbules NC, Schraw G, and Trathen W Metaphor, idiom, and figuration Metaphor Symb 1989 4 2 93-110

[20]

Cabot PLH, Dankers V, Abadi D, et al. The pragmatics behind politics: modelling metaphor, framing and emotion in political discourse Find Assoc Comput Linguist 2020 2020 4479-4488

[21]

Cambria E, Liu Q, Decherchi S, et al (2022a) SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In: Proceedings of the 13th conference on language resources and evaluation (LREC), pp 3829–3839

[22]

Cambria E, Mao R, Han S, et al (2022b) Sentic parser: a graph-based approach to concept extraction for sentiment analysis. In: 2022 international conference on data mining workshops (ICDMW). IEEE, Orlando, pp 413–420, https://sentic.net/sentic-parser.pdf

[23]

Chakrabarty T, Zhang X, Muresan S, et al (2021) MERMAID: metaphor generation with symbolism and discriminative decoding. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 4250–4261

[24]

Chen X, Leong CW, Flor M, et al (2020) Go figure! multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task. In: Proceedings of the second workshop on figurative language processing, pp 235–243

[25]

Chen X, Hai Z, Wang S, et al. Metaphor identification: a contextual inconsistency based neural sequence labeling approach Neurocomputing 2021 428 268-279

[26]

Choi M, Lee S, Choi E, et al (2021) MelBERT: metaphor detection via contextualized late interaction using metaphorical identification theories. In: 2021 conference of the North American chapter of the association for computational linguistics: human language technologies

[27]

Cohen J A coefficient of agreement for nominal scales Educ Psychol Measur 1960 20 1 37-46

[28]

Consortium B British national corpus 2007 Oxford Oxford Text Archive Core Collection

[29]

Consortium LD (2011) Spanish Gigaword, 3rd edn. Linguistic Data Consortium

[30]

Do Dinh EL, Gurevych I (2016) Token-level metaphor detection using neural networks. In: Proceedings of the fourth workshop on metaphor in NLP, pp 28–33

[31]

Dodge EK, Hong J, Stickles E (2015) MetaNet: Deep semantic automatic metaphor analysis. In: Proceedings of the third workshop on metaphor in NLP, pp 40–49

[32]

Duong C, Liu Q, Mao R, et al (2022) Saving earth one tweet at a time through the lens of artificial intelligence. In: 2022 International joint conference on neural networks (IJCNN), Padua, pp 1–9,

[33]

Fleiss JL Measuring nominal scale agreement among many raters Psychol Bull 1971 76 5 378

[34]

Francis WN and Kucera H Brown corpus manual Lett Editor 1979 5 2 7

[35]

Fu C, Wang J, Sang J, et al (2020) Beyond literal visual modeling: Understanding image metaphor based on literal-implied concept mapping. In: International conference on multimedia modeling, Springer, pp 111–123

[36]

Gagliano A, Paul E, Booten K, et al (2016) Intersecting word vectors to take figurative language to new heights. In: Proceedings of the fifth workshop on computational linguistics for literature, pp 20–31

[37]

Gallant SI A practical approach for representing context and for performing word sense disambiguation using neural networks Neural Comput 1991 3 3 293-309

[38]

Gandy L, Allan N, Atallah M, et al (2013) Automatic identification of conceptual metaphors with limited knowledge. In: Twenty-Seventh AAAI Conference on Artificial Intelligence

[39]

Gao G, Choi E, Choi Y, et al (2018) Neural metaphor detection in context. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 607–613

[40]

Ge M, Mao R, and Cambria E Explainable metaphor identification inspired by conceptual metaphor theory Proc AAAI Conf Artif Intell 2022 36 10 10,681-10,689

[41]

Gong H, Gupta K, Jain A, et al (2020) IlliniMet: Illinois system for metaphor detection with contextual and linguistic information. In: Proceedings of the second workshop on figurative language processing, pp 146–153

[42]

Graff D, Cieri C (2003) English gigaword, linguistic data consortium

[43]

Gutierrez ED, Shutova E, Marghetis T, et al (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), pp 183–193

[44]

Han S, Mao R, Cambria E (2022) Hierarchical attention network for explainable depression detection on Twitter aided by metaphor concept mappings. In: Proceedings of the 29th international conference on computational linguistics (COLING). International committee on computational linguistics, Gyeongju, Republic of Korea, pp 94–104

[45]

Hazarika D, Poria S, Gorantla S, et al (2018) CASCADE: Contextual sarcasm detection in online discussion forums. In: Proceedings of the 27th international conference on computational linguistics, pp 1837–1848

[46]

Hu R, Wang X (2021) A cognitive pragmatic analysis of conceptual metaphor in political discourse based on text data mining. In: 2021 4th international conference on information systems and computer aided education, pp 235–238

[47]

Indurkhya B Metaphor and cognition: an interactionist approach 2013 New York Springer

[48]

Jacobs AM The Gutenberg English poetry corpus: exemplary quantitative narrative analyses Front Digital Hum 2018 5 5

[49]

Kehat G, Pustejovsky J (2021) Neural metaphor detection with visibility embeddings. In: Proceedings of* SEM 2021: the tenth joint conference on lexical and computational semantics, pp 222–228

[50]

Kenton JDMWC, Toutanova LK (2019) BERT: Pre-training of deep bidirectional Transformers for language understanding. In: Proceedings of the 17th annual conference of the north american chapter of the association for computational linguistics: human language technologies (NAACL-HLT 2019), pp 4171–4186

[51]

Klebanov BB, Leong B, Heilman M, et al (2014) Different texts, same metaphors: Unigrams and beyond. In: Proceedings of the second workshop on metaphor in NLP, pp 11–17

[52]

Klebanov BB, Leong CW, Gutierrez ED, et al (2016) Semantic classifications for detection of verb metaphors. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers), pp 101–106

[53]

Klebanov BB, Leong CW, Flor M (2018) A corpus of non-native written English annotated for metaphor. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 2 (short papers), pp 86–91

[54]

Kovecses Z Metaphor: a practical introduction 2010 Oxford Oxford University Press

[55]

Krippendorff K (2011) Computing Krippendorff’s alpha-reliability. University of Pennsylvania, Technical report

[56]

Krishnakumaran S, Zhu X (2007) Hunting elusive metaphors using lexical resources. In: Proceedings of the workshop on computational approaches to figurative language, pp 13–20

[57]

Lakoff G Master metaphor list 1994 California University of California

[58]

Lakoff G and Johnson M Metaphors we live by 1980 Chicago University of Chicago Press

[59]

Le D, Thai M, Nguyen T (2020) Multi-task learning for metaphor detection with graph convolutional neural networks and word sense disambiguation. In: Proceedings of the AAAI conference on artificial intelligence, pp 8139–8146

[60]

Leong CW, Klebanov BB, Shutova E (2018) A report on the 2018 VUA metaphor detection shared task. In: Proceedings of the workshop on figurative language processing, pp 56–66

[61]

Leong CW, Klebanov BB, Hamill C, et al (2020) A report on the 2020 VUA and TOEFL metaphor detection shared task. In: Proceedings of the second workshop on figurative language processing, pp 18–29

[62]

Levesque H, Davis E, Morgenstern L (2012) The Winograd schema challenge. In: Thirteenth international conference on the principles of knowledge representation and reasoning

[63]

Lewis M, Liu Y, Goyal N, et al (2020) BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7871–7880

[64]

Li H, Zhu KQ, and Wang H Data-driven metaphor recognition and explanation Trans Assoc Comput Linguist 2013 1 379-390

[65]

Li L, Sporleder C (2009) Classifier combination for contextual idiom detection without labelled data. In: Proceedings of the 2009 conference on empirical methods in natural language processing, pp 315–323

[66]

Li S, Yang L, He W, et al (2021) Label-enhanced hierarchical contextualized representation for sequential metaphor identification. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 3533–3543

[67]

Li Y, Lin C, Guerin F (2022) CM-Gen: A neural framework for Chinese metaphor generation with explicit context modelling. In: Proceedings of the 29th international conference on computational linguistics, pp 6468–6479

[68]

Lin Q, Mao R, Liu J, et al. Fusing topology contexts and logical rules in language models for knowledge graph completion Inform Fusion 2023 90 253-264

[69]

Lin Z, Ma Q, Yan J, et al (2021) CATE: A contrastive pre-trained model for metaphor detection with semi-supervised learning. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 3888–3898

[70]

Liu E, Cui C, Zheng K, et al (2022) Testing the ability of language models to interpret figurative language. arXiv e-prints pp arXiv-2204

[71]

Liu Y, Ott M, Goyal N, et al (2019) RoBERTa: a robustly optimized BERT pretraining approach. arXiv e-prints pp arXiv-1907

[72]

Lönneker-Rodman B The Hamburg metaphor database project: issues in resource creation Lang Resour Eval 2008 42 3 293-318

[73]

Ma Y, Mao R, Lin Q, et al. Multi-source aggregated classification for stock price movement prediction Inform Fus 2023 91 515-528

[74]

Mao R (2020) Computational metaphor processing. PhD thesis, University of Aberdeen, Scotland

[75]

Mao R, Li X (2021) Bridging towers of multi-task learning with a gating mechanism for aspect-based sentiment analysis and sequential metaphor identification. In: Proceedings of the AAAI conference on artificial intelligence, pp 13534–13542

[76]

Mao R, Lin C, Guerin F (2018) Word embedding and WordNet based metaphor identification and interpretation. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 1222–1231

[77]

Mao R, Lin C, Guerin F (2019) End-to-end sequential metaphor identification inspired by linguistic theories. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 3888–3898

[78]

Mao R, Li X, Ge M, et al. MetaPro: a computational metaphor processing model for text pre-processing Inform Fus 2022 86–87 30-43

[79]

Mao R, Liu Q, He K, et al (2022b) The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection. IEEE transactions on affective computing

[80]

Mao R, Li X, He K, et al (2023) MetaPro online: a computational metaphor processing online system. In: Proceedings of the 61th annual meeting of the association for computational linguistics (system demonstrations), pp 127–135

[81]

Martin JH A computational model of metaphor interpretation 1990 Boston Academic Press Professional, Inc

[82]

Mason ZJ CorMet: a computational, corpus-based conventional metaphor extraction system Comput Linguist 2004 30 1 23-44

[83]

Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality Adv Neural Inform Process Syst 2013 89 59

[84]

Miller GA WordNet: an electronic lexical database 1998 Cambridge MIT Press

[85]

Mohammad S, Shutova E, Turney P (2016) Metaphor as a medium for emotion: an empirical study. In: Proceedings of the fifth joint conference on lexical and computational semantics, pp 23–33

[86]

Mohler M, Bracewell D, Tomlinson M, et al (2013) Semantic signatures for example-based linguistic metaphor detection. In: Proceedings of the first workshop on metaphor in NLP, pp 27–35

[87]

Mohler M, Brunson M, Rink B, et al (2016) Introducing the LCC metaphor datasets. In: Proceedings of the tenth international conference on language resources and evaluation (LREC’16), pp 4221–4227

[88]

Osbeck LM and Nersessian NJ Science as psychology: sense-making and identity in science practice 2010 Cambridge Cambridge University Press

[89]

Ottolina G, Palmonari M, Alam M, et al (2021) On the impact of temporal representations on metaphor detection. arXiv preprint arXiv:2111.03320

[90]

Papineni K, Roukos S, Ward T, et al (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311–318

[91]

Pennington J, Socher R, Manning CD (2014) GloVe: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

[92]

Peters ME, Neumann M, Iyyer M, et al (2018) Deep contextualized word representations. arxiv preprint. arXiv preprint arXiv:1802.05365

[93]

Prabhakaran V, Rei M, Shutova E (2021) How metaphors impact political discourse: a large-scale topic-agnostic study using neural metaphor detection. arXiv preprint arXiv:2104.03928

[94]

Pragglejaz G MIP: a method for identifying metaphorically used words in discourse Metaphor Symb 2007 22 1 1-39

[95]

Qin W, Zhao D (2021) Background semantic information improves verbal metaphor identification. In: CCF international conference on natural language processing and Chinese computing, Springer, pp 288–300

[96]

Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners OpenAI blog 2019 1 8 9

[97]

Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer J Mach Learn Res 2020 21 1-67

[98]

Rai S and Chakraverty S A survey on computational metaphor processing ACM Comput Surv (CSUR) 2020 53 2 1-37

[99]

Rai S, Chakraverty S, Tayal DK, et al. Understanding metaphors using emotions New Gener Comput 2019 37 1 5-27

[100]

Ratinov L, Roth D (2009) Design challenges and misconceptions in named entity recognition. In: Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009), pp 147–155

[101]

Rei M, Bulat L, Kiela D, et al (2017) Grasping the finer point: a supervised similarity network for metaphor detection. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 1537–1546

[102]

Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3982–3992

[103]

Ren L, Xu B, Lin H, et al. ABML: attention-based multi-task learning for jointly humor recognition and pun detection Soft Comput 2021 25 22 14,109-14,118

[104]

Ritter A, Etzioni O, et al (2010) A latent Dirichlet allocation method for selectional preferences. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp 424–434

[105]

Rohanian O, Rei M, Taslimipoor S, et al (2020) Verbal multiword expressions for identification of metaphor. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 2890–2895

[106]

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, pp 102–109

[107]

Rundell M and Fox GE Macmillan English dictionary for advanced learners Korea TESOL J 2002 5 1 183-187

[108]

Sam G and Catrinel H On the relation between metaphor and simile: when comparison fails Mind Lang 2006 21 3 360-378

[109]

Satopaa V, Albrecht J, Irwin D, et al (2011) Finding a “kneedle” in a haystack: Detecting knee points in system behavior. In: 2011 31st International conference on distributed computing systems workshops, IEEE, pp 166–171

[110]

Schuler KK VerbNet: a broad-coverage 2005 Philadelphia University of Pennsylvania

[111]

Shakespeare W (2019) As you like it. In: One-hour Shakespeare. Routledge, p 56

[112]

Sharma C, Bhageria D, Scott W, et al (2020) SemEval-2020 task 8: memotion analysis-the visuo-lingual metaphor! arXiv preprint arXiv:2008.03781

[113]

Shutova E (2010) Automatic metaphor interpretation as a paraphrasing task. In: Human language technologies: the 2010 annual conference of the North American chapter of the association for computational linguistics, pp 1029–1037

[114]

Shutova E Design and evaluation of metaphor processing systems Comput Linguist 2015 41 4 579-623

[115]

Shutova E, Teufel S (2010) Metaphor corpus annotated for source-target domain mappings. In: Proceedings of the seventh international conference on language resources and evaluation (LREC’10)

[116]

Shutova E, Kiela D, Maillard J (2016) Black holes and white rabbits: Metaphor identification with visual features. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 160–170

[117]

Sivakumar V, Gordo A, and Paluri M Rosetta: understanding text in images and videos with machine learning Facebook Eng Blog Posted 2018 11 2018

[118]

Socher R, Perelygin A, Wu J, et al (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642

[119]

Song W, Guo J, Fu R, et al. A knowledge graph embedding approach for metaphor processing IEEE/ACM Trans Audio Speech Lang Process 2020 29 406-420

[120]

Song W, Zhou S, Fu R, et al (2021) Verb metaphor detection via contextual relation learning. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pp 4240–4251

[121]

Steen G, Dorst L, Herrmann B, et al (2010a) A method for linguistic metaphor identification from MIP to MIPVU preface. Method for linguistic metaphor identification: from MIP To MIPVU 14:IX-+

[122]

Steen GJ, Dorst AG, Herrmann JB, et al. Metaphor in usage Cogn Linguist 2010 21 4 765-796

[123]

Stemle E, Onysko A (2018) Using language learner data for metaphor detection. In: Proceedings of the workshop on figurative language processing, pp 133–138

[124]

Stowe K, Moeller S, Michaelis L, et al (2019) Linguistic analysis improves neural metaphor detection. In: Proceedings of the 23rd conference on computational natural language learning (CoNLL), pp 362–371

[125]

Stowe K, Ribeiro L, Gurevych I (2020) Metaphoric paraphrase generation. arXiv preprint arXiv:2002.12854

[126]

Stowe K, Beck N, Gurevych I (2021a) Exploring metaphoric paraphrase generation. In: Proceedings of the 25th conference on computational natural language learning, pp 323–336

[127]

Stowe K, Chakrabarty T, Peng N, et al (2021b) Metaphor generation with conceptual mappings. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pp 6724–6736

[128]

Strzalkowski T, Broadwell GA, Taylor S, et al (2013) Robust extraction of metaphor from novel data. In: Proceedings of the first workshop on metaphor in NLP, pp 67–76

[129]

Su C, Huang S, Chen Y (2015) Context-dependent metaphor interpretation based on semantic relatedness. In: Natural language processing and Chinese computing. Springer, pp 182–193

[130]

Su C, Huang S, and Chen Y Automatic detection and interpretation of nominal metaphor based on the theory of meaning Neurocomputing 2017 219 300-311

[131]

Su C, Fukumoto F, Huang X, et al (2020a) DeepMet: a reading comprehension paradigm for token-level metaphor detection. In: Proceedings of the second workshop on figurative language processing, pp 30–39

[132]

Su C, Peng Y, Huang S, et al. A metaphor comprehension method based on culture-related hierarchical semantic model Neural Process Lett 2020 51 3 2807-2826

[133]

Su C, Chen W, Fu Z, et al. Multimodal metaphor detection based on distinguishing concreteness Neurocomputing 2021 429 166-173

[134]

Su C, Wu K, and Chen Y Enhanced metaphor detection via incorporation of external knowledge based on linguistic theories Find Assoc Comput Linguist: ACL-IJCNLP 2021 2021 1280-1287

[135]

Sweetser E From etymology to pragmatics: metaphorical and cultural aspects of semantic structure 1990 Cambridge Cambridge University Press

[136]

Tasić M and Stamenković D The interplay of words and images in expressing multimodal metaphors in comics Procedia 2015 212 117-122

[137]

Tileagă C Political psychology: critical perspectives 2013 Cambridge Cambridge University Press

[138]

Tong X, Shutova E, Lewis M (2021) Recent advances in neural metaphor processing: a linguistic, cognitive and social perspective. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 4673–4686

[139]

Tsvetkov Y, Mukomel E, Gershman A (2013) Cross-lingual metaphor detection using common semantic features. In: Proceedings of the first workshop on metaphor in NLP, pp 45–51

[140]

Tsvetkov Y, Boytsov L, Gershman A, et al (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), pp 248–258

[141]

Turbayne CM The myth of metaphor Br J Philos Sci 1964 571964 15

[142]

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inform Process Syst 2017 30 8

[143]

Wan H, Lin J, Du J, et al. Enhancing metaphor detection by gloss-based interpretations Find Assoc Computa Linguist ACL-IJCNLP 2021 2021 1971-1981

[144]

Wan M, Xing B, Su Q, et al (2020) Sensorimotor enhanced neural network for metaphor detection. In: Proceedings of the 34th pacific Asia conference on language, information and computation, pp 312–317

[145]

Wilks Y A preferential, pattern-seeking, semantics for natural language inference Artif Intell 1975 6 1 53-74

[146]

Wilks Y Making preferences more active Artif Intell 1978 11 3 197-223

[147]

Xing F, Malandri L, Zhang Y, et al (2020) Financial sentiment analysis: an investigation into common mistakes and silver bullets. In: Proceedings of the 28th international conference on computational linguistics (COLING), pp 978–987

[148]

Xu B, Li T, Zheng J, et al (2022) MET-Meme: a multimodal meme dataset rich in metaphors. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 2887–2899

[149]

Yang L, Zeng J, Li S, et al (2021) Metaphor recognition and analysis via data augmentation. In: CCF international conference on natural language processing and Chinese computing, Springer, pp 746–757

[150]

Yu Z, Wan X (2019) How to avoid sentences spelling boring? Towards a neural approach to unsupervised metaphor generation. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp 861–871

[151]

Zayed O, McCrae JP, Buitelaar P (2018) Phrase-level metaphor identification using distributed representations of word meaning. In: proceedings of the workshop on figurative language processing, pp 81–90

[152]

Zayed O, McCrae JP, Buitelaar P (2019) Crowd-sourcing a high-quality dataset for metaphor identification in tweets. In: 2nd conference on language, data and knowledge (LDK 2019), Schloss Dagstuhl–Leibniz–Zentrum fuer Informatik

[153]

Zayed O, McCrae JP, and Buitelaar P Contextual modulation for relation-level metaphor identification Find Assoc Comput Linguist EMNLP 2020 2020 388-406

[154]

Zayed O, McCrae JP, Buitelaar P (2020b) Figure me out: a gold standard dataset for metaphor interpretation. In: Proceedings of the 12th language resources and evaluation conference, pp 5810–5819

[155]

Zhang D, Shi N, Peng C, et al (2021a) MAM: a metaphor-based approach for mental illness detection. In: International conference on computational science, Springer, pp 570–583

[156]

Zhang D, Zhang M, Zhang H, et al (2021b) MultiMET: a multimodal dataset for metaphor understanding. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pp 3214–3225

[157]

Zhang T, Kishore V, Wu F, et al (2019) BERTScore: evaluating text generation with BERT. In: international conference on learning representations

[158]

Zhao W, Peyrard M, Liu F, et al (2019) MoverScore: text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 563–578

[159]

Zheng D, Song R, Hu T, et al (2019) “Love is as complex as math”: Metaphor generation system for social chatbot. In: Workshop on Chinese lexical semantics, Springer, pp 337–347

Cited By

Mao RHe KZhang XChen GNi JYang ZCambria E(2024)A survey on semantic processing techniquesInformation Fusion10.1016/j.inffus.2023.101988101:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.101988
Puraivan ERenau IRiquelme N(2024)Metaphor Identification and Interpretation in Corpora with ChatGPTSN Computer Science10.1007/s42979-024-03331-05:8Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03331-0
Liu HHe CMeng FNiu CJia Y(2024)LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components IdentificationNatural Language Processing and Chinese Computing10.1007/978-981-97-9443-0_25(287-299)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1007/978-981-97-9443-0_25

Recommendations

A Survey on Computational Metaphor Processing

In the last decade, the problem of computational metaphor processing has garnered immense attention from the domains of computational linguistics and cognition. A wide panorama of approaches, ranging from a hand-coded rule system to deep learning ...
Knowledge based metaphor interpretation

In this paper we outline a computational model to interpret linguistic metaphors. In our computational model, we first obtain a syntactic parser's output for an utterance and then check if there is any strain on the primary meaning to recognise the ...
MetaPro: A computational metaphor processing model for text pre-processing
Abstract
Metaphor is a special linguistic phenomenon, challenging diverse natural language processing tasks. Previous works focused on either metaphor identification or domain-specific metaphor interpretation, e.g., interpreting metaphors with ...
Highlights
- MetaPro fuses metaphor detection and interpretation modules in an end-to-end system.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 56, Issue Suppl 2

Nov 2023

1281 pages

ISSN:0269-2821

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 11 August 2023

Author Tags

Qualifiers

Research-article

Funding Sources

Industry Alignment Fund Industry Collaboration Projects (IAF-ICP)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mao RHe KZhang XChen GNi JYang ZCambria E(2024)A survey on semantic processing techniquesInformation Fusion10.1016/j.inffus.2023.101988101:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.101988
Puraivan ERenau IRiquelme N(2024)Metaphor Identification and Interpretation in Corpora with ChatGPTSN Computer Science10.1007/s42979-024-03331-05:8Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03331-0
Liu HHe CMeng FNiu CJia Y(2024)LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components IdentificationNatural Language Processing and Chinese Computing10.1007/978-981-97-9443-0_25(287-299)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1007/978-981-97-9443-0_25

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents