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Social Network Analysis and Mining

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01-12-2023 | Original Article

Exploiting stacked embeddings with LSTM for multilingual humor and irony detection

Authors: Radiathun Tasnia, Nabila Ayman, Afrin Sultana, Abu Nowshed Chy, Masaki Aono

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article delves into the complexities of detecting humor and irony in multilingual texts, highlighting the use of stacked embeddings with LSTM to capture diverse contexts. It discusses the challenges of humor and irony detection, the proposed stacked embeddings approach, and the experimental results on various benchmark datasets. The use of multiple word and transformer embeddings, along with document LSTM embeddings, is a key focus, demonstrating the effectiveness of the proposed method in capturing the nuances of humor and irony in texts.

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https://docs.fast.ai/

https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings.

https://github.com/NeelShah18/emot.

https://github.com/rmit-ir/KrovetzStemmer.

https://pypi.org/project/unicode/.

Akbik A, Blythe D, Vollgraf R (2018) Contextual string embeddings for sequence labeling. In: Proceedings of the 27th international conference on computational linguistics, pp 1638–1649. https://aclanthology.org/C18-1139/

Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET), IEEE, pp 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186

Altin LSM, Bravo À, Saggion H (2019) Lastus/taln at haha: Humor analysis based on human annotation. In: IberLEF@ SEPLN, pp 145–150

Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. J Big Data 8:1–74. https://doi.org/10.1186/s40537-021-00444-8CrossRef

Amir S, Wallace BC, Lyu H, et al (2016) Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976https://doi.org/10.48550/arXiv.1607.00976

Annamoradnejad I, Zoghi G (2020) Colbert: Using bert sentence embedding for humor detection. arXiv preprint arXiv:2004.12765https://doi.org/10.48550/arXiv.2004.12765

Annamoradnejad I, Zoghi G (2021) Colbert at haha 2021: Parallel neural networks for rating humor in spanish tweets. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2021), CEUR workshop proceedings, Málaga, Spain

Barbieri F, Saggion H (2014) Modelling irony in twitter. In: Proceedings of the student research workshop at the 14th conference of the european chapter of the association for computational linguistics, pp 56–64. https://doi.org/10.3115/v1/e14-3007

Baziotis C, Pelekis N, Doulkeridis C (2017) Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), pp 747–754. https://doi.org/10.18653/v1/S17-2126

Baziotis C, Athanasiou N, Papalampidi P, et al (2018) Ntua-slp at semeval-2018 task 3: Tracking ironic tweets using ensembles of word and character level attentive rnns. arXiv preprint arXiv:1804.06659https://doi.org/10.48550/arXiv.1804.06659

Bharti SK, Babu KS, Jena SK (2015) Parsing-based sarcasm sentiment recognition in twitter data. In: 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 1373–1380. https://doi.org/10.1145/2808797.2808910

Bojanowski P, Grave E, Joulin A et al (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguistics 5:135–146. https://doi.org/10.1162/tacl_a_00051CrossRef

BREDIN H (1997) The semantic structure of verbal irony. J Literary Semantics 26(1):1–20CrossRef

Brône G (2017) Cognitive linguistics and humor research. In: The Routledge handbook of language and humor. Routledge, pp 250–266

Brône G (2021) The multimodal negotiation of irony and humor in interaction. Figurative Language Intersubject Usage 11:109CrossRef

Brône G, Feyaerts K, Veale T (2006) Introduction: cognitive linguistic approaches to humor. Humor Int J Humor Res 19(3):203–228

Buschmeier K, Cimiano P, Klinger R (2014) An impact analysis of features in a classification approach to irony detection in product reviews. In: Proceedings of the 5th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 42–49. https://doi.org/10.3115/v1/w14-2608

Canete J, Chaperon G, Fuentes R, et al (2020) Spanish pre-trained bert model and evaluation data. Pml4dc at iclr 2020:2020

Castro S, Chiruzzo L, Rosá A (2018) Overview of the haha task: Humor analysis based on human annotation at ibereval 2018. In: IberEval@ SEPLN, pp 187–194

Cattle AG, Zhao Z, Papalexakis EE, et al (2019) Generating document embeddings for humor recognition using tensor decomposition. In: CEUR workshop proceedings, p 151

Cañete J, Chaperon G, Fuentes R, et al (2020) Spanish pre-trained bert model and evaluation data. In: PML4DC at ICLR 2020

Chi N, Chi R (2021) Redwoodnlp at semeval-2021 task 7: Ensembled pretrained and lightweight models for humor detection. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1209–1214. https://doi.org/10.18653/v1/2021.semeval-1.171

Chiruzzo L, Castro S, Etcheverry M, et al (2019) Overview of haha at iberlef 2019: Humor analysis based on human annotation. In: IberLEF@ SEPLN, pp 132–144

Chiruzzo L, Castro S, Góngora S, et al (2021) Overview of HAHA at IberLEF 2021: detecting, rating and analyzing humor in Spanish. Procesamiento del Lenguaje Natural 67

Chung J, Gulcehre C, Cho K, et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555https://doi.org/10.48550/arXiv.1412.3555

CLARK H (1984) On the pretense theory of irony. J Exp Psychol General 113:121–126CrossRef

Colston HL, Gibbs RW Jr (1998) Analogy and irony: rebuttal to “rebuttal analogy’’. Metaphor Symbol 13(1):69–75CrossRef

Davidson T, Warmsley D, Macy M, et al (2017) Automated hate speech detection and the problem of offensive language. In: Proceedings of the international AAAI conference on web and social media, pp 512–515. https://doi.org/10.48550/arXiv.1703.04009

de Arriba Serra A, Oriol Hilari M, Franch Gutiérrez J (2021) Applying sentiment analysis on spanish tweets using beto. In: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021): co-located with the Conference of the Spanish Society for Natural Language Processing (SEPLN 2021), XXXVII international conference of the Spanish society for natural language processing: Málaga, Spain, September, 2021, CEUR-WS. org, pp 1–8. http://hdl.handle.net/2117/356656

Desai S, Goh G, Babu A, et al (2020) Lightweight convolutional representations for on-device natural language processing. arXiv preprint arXiv:2002.01535https://doi.org/10.48550/arXiv.2002.01535

Devlin J, Chang MW, Lee K, et al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805https://doi.org/10.48550/arXiv.1810.04805

Devlin J, Chang MW, Lee K (2019) Kristina, toutanova. Bert: pre-training of deep bidirectional, transformers for language understanding. In: NAACL 2(4):5

Faraj D, Abdullah M (2021) Sarcasmdet at semeval-2021 task 7: Detect humor and offensive based on demographic factors using roberta pre-trained model. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 527–533. https://doi.org/10.18653/v1/2021.semeval-1.64

Farzin B, Czapla P, Howard J (2019) Applying a pre-trained language model to spanish twitter humor prediction. arXiv preprint arXiv:1907.03187https://doi.org/10.48550/arXiv.1907.03187

Ghosh A, Li G, Veale T, et al (2015) Semeval-2015 task 11: sentiment analysis of figurative language in twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 470–478. https://doi.org/10.18653/v1/s15-2080

Giora R, Fein O (1999) Irony: context and salience. Metaphor Symbol 14(4):241–257CrossRef

Giudice V (2019) Aspie96 at haha (iberlef 2019): Humor detection in spanish tweets with character-level convolutional rnn. In: IberLEF@ SEPLN, pp 165–171

González JÁ, Hurtado LF, Pla F (2018) Elirf-upv at semeval-2018 tasks 1 and 3: affect and irony detection in tweets. In: Proceedings of The 12th international workshop on semantic evaluation, pp 565–569. https://doi.org/10.18653/v1/s18-1092

González-Ibánez R, Muresan S, Wacholder N (2011) Identifying sarcasm in twitter: a closer look. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 581–586

Graff M, Tellez ES, Miranda-Jiménez S, et al (2016) Evodag: a semantic genetic programming python library. In: 2016 IEEE international autumn meeting on power, electronics and computing (ROPEC), IEEE, pp 1–6. https://doi.org/10.1109/ROPEC.2016.7830633

Graff M, Miranda-Jimenez S, Tellez ES et al (2020) Evomsa: a multilingual evolutionary approach for sentiment analysis [application notes]. IEEE Comput Intell Mag 15(1):76–88. https://doi.org/10.1109/MCI.2019.2954668CrossRef

Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional lstm networks. In: Proceedings. 2005 IEEE international joint conference on neural networks, 2005., IEEE, pp 2047–2052. https://doi.org/10.1109/IJCNN.2005.1556215

Grover K, Goel T (2021) Haha@ iberlef2021: Humor analysis using ensembles of simple transformers. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2021), CEUR Workshop Proceedings, Málaga, Spain

Guan Z, Zhou XZ (2021) Tsia at semeval-2021 task 7: Detecting and rating humor and offense. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1108–1113. https://doi.org/10.18653/v1/2021.semeval-1.154

Han B, Cook P, Baldwin T (2012) Automatically constructing a normalisation dictionary for microblogs. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp 421–432. https://aclanthology.org/D12-1039

Hasan MK, Lee S, Rahman W, et al (2021) Humor knowledge enriched transformer for understanding multimodal humor. In: Proceedings of the AAAI conference on artificial intelligence, pp 12972–12980

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

Hoicka E (2014) The pragmatic development of humor. Prag Develop First Lang Acquisit 10:219

Howard J, Gugger S (2020) Fastai: a layered api for deep learning. Information 11(2):108. https://doi.org/10.3390/info11020108CrossRef

Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146https://doi.org/10.48550/arXiv.1801.06146

Huang HH, Chen CC, Chen HH (2018) Disambiguating false-alarm hashtag usages in tweets for irony detection. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 2: Short Papers), pp 771–777. https://doi.org/10.18653/v1/p18-2122

Hulstijn J (1996) Automatic interpretation and generation of verbal humor. In: Proceedings o the IWCH96

Ilić S, Marrese-Taylor E, Balazs JA, et al (2018) Deep contextualized word representations for detecting sarcasm and irony. arXiv preprint arXiv:1809.09795https://doi.org/10.48550/arXiv.1809.09795

Ismailov A (2019) Humor analysis based on human annotation challenge at iberlef 2019: first-place solution. In: IberLEF@ SEPLN, pp 160–164

Joshi A, Sharma V, Bhattacharyya P (2015) Harnessing context incongruity for sarcasm detection. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 2: Short Papers), pp 757–762. https://doi.org/10.3115/v1/p15-2124

Jr RWG, O’Brien JE, Doolittle S (1995) Inferring meanings that are not intended: Speakers’ intentions and irony comprehension. Discourse Process 20(2):187–203. https://doi.org/10.1080/01638539509544937

Khandelwal A, Swami S, Akhtar SS, et al (2018) Humor detection in english-hindi code-mixed social media content: Corpus and baseline system. arXiv preprint arXiv:1806.05513https://doi.org/10.48550/arXiv.1806.05513

Kui Y (2021) Applying pre-trained model and fine-tune to conduct humor analysis on spanish tweets. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2021), CEUR workshop proceedings, Málaga, Spain

Lan Z, Chen M, Goodman S, et al (2019) Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942https://doi.org/10.48550/arXiv.1909.11942

Liebrecht C, Kunneman F, van den Bosch A (2013) The perfect solution for detecting sarcasm in tweets# not. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 29–37

Liu F, Weng F, Jiang X (2012) A broad-coverage normalization system for social media language. In: Proceedings of the 50th annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 1035–1044. https://aclanthology.org/P12-1109

Liu P, Li W, Zou L (2019a) Nuli at semeval-2019 task 6: Transfer learning for offensive language detection using bidirectional transformers. In: SemEval@ NAACL-HLT, pp 87–91, https://doi.org/10.18653/v1/s19-2011

Liu Y, Ott M, Goyal N, et al (2019b) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692https://doi.org/10.48550/arXiv.1907.11692

Loper E, Bird S (2002) Nltk: the natural language toolkit. arXiv preprint cs/0205028 https://doi.org/10.48550/arXiv.cs/0205028

Lozano-Palacio I, de Mendoza Ibáñez FJR (2022) Modeling Irony: a cognitive-pragmatic account. John Benjamins

Mao J, Liu W (2019) A bert-based approach for automatic humor detection and scoring. In: IberLEF@ SEPLN, pp 197–202

Maynard DG, Greenwood MA (2014) Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Lrec 2014 proceedings, ELRA

Meaney J, Wilson SR, Chiruzzo L, et al (2021) Semeval 2021 task 7, hahackathon, detecting and rating humor and offense. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing. https://doi.org/10.18653/v1/2021.semeval-1.9

Meyer JC (2000) Humor as a double-edged sword: four functions of humor in communication. Commun Theory 10(3):310–331. https://doi.org/10.1111/j.1468-2885.2000.tb00194.xCrossRef

Miller T, Do Dinh EL, Simpson E, et al (2019) Ofai-ukp at haha@ iberlef2019: predicting the humorousness of tweets using gaussian process preference learning. In: IberLEF@ SEPLN, pp 180–190

Miyato T, Dai AM, Goodfellow I (2016) Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725https://doi.org/10.48550/arXiv.1605.07725

Mondal A, Sharma R (2021) Team_kgp at semeval-2021 task 7: a deep neural system to detect humor and offense with their ratings in the text data. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1169–1174. https://doi.org/10.18653/v1/2021.semeval-1.164

Nanda A, Singh AP, Gupta A, et al (2021) Techssn at haha@ iberlef 2021: Humor detection and funniness score prediction using deep learning. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2021), CEUR workshop proceedings, Málaga, Spain

Ortega-Bueno R, Muniz-Cuza CE, Pagola JEM, et al (2018) Uo upv: Deep linguistic humor detection in spanish social media. In: Proceedings of the third workshop on evaluation of human language technologies for Iberian languages (IberEval 2018) co-located with 34th conference of the Spanish society for natural language processing (SEPLN 2018), pp 204–213

Ortega-Bueno R, Rosso P, Pagola JEM (2019) Uo upv2 at haha 2019: Bigru neural network informed with linguistic features for humor recognition. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019). CEUR workshop proceedings, CEUR-WS, Bilbao, Spain (9 2019)

Ortiz-Bejar J, Salgado V, Graff M, et al (2018) Ingeotec at ibereval 2018 task haha: \(\mu\)tc and evomsa to detect and score humor in texts. In: Proceedings of the third workshop on evaluation of human language technologies for Iberian languages (IberEval 2018) co-located with 34th conference of the Spanish sSociety for natural language processing (SEPLN 2018)

Ortiz-Bejar J, Tellez ES, Graff M, et al (2019) Ingeotec at iberlef 2019 task haha. In: IberLEF@ SEPLN, pp 203–211

Padró L, Stanilovsky E (2012) Freeling 3.0: towards wider multilinguality. In: LREC2012. http://hdl.handle.net/2117/15986

Pamungkas EW, Patti V (2018) # nondicevosulserio at semeval-2018 task 3: Exploiting emojis and affective content for irony detection in english tweets. In: International workshop on semantic evaluation, Association for Computational Linguistics, pp 649–654. https://doi.org/10.18653/v1/s18-1106

Pannu A (2015) Artificial intelligence and its application in different areas. Artif Intell 4(10):79–84

Peña MS, Ruiz de Mendoza FJ (2017) Construing and constructing hyperbole. Stud Figurative Thought Lang 56:41

Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates 71(2001):2001

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. https://doi.org/10.3115/v1/d14-1162

Peters ME, Neumann M, Iyyer M, et al (2018) Deep contextualized word representations. In: Proceedings of the NAACL. https://doi.org/10.18653/v1/n18-1202

Raghunathan A, Xie SM, Yang F, et al (2019) Adversarial training can hurt generalization. arXiv preprint arXiv:1906.06032https://doi.org/10.48550/arXiv.1906.06032

Rangwani H, Kulshreshtha D, Singh AK (2018) Nlprl-iitbhu at semeval-2018 task 3: combining linguistic features and emoji pre-trained cnn for irony detection in tweets. In: Proceedings of the 12th international workshop on semantic evaluation, pp 638–642. https://doi.org/10.18653/v1/s18-1104

Reyes A, Rosso P, Buscaldi D (2012) From humor recognition to irony detection: The figurative language of social media. Data Knowl Eng 74:1–12. https://doi.org/10.1016/j.datak.2012.02.005, www.sciencedirect.com/science/article/pii/S0169023X12000237, applications of Natural Language to Information Systems

Reyes A, Rosso P, Buscaldi D (2012) From humor recognition to irony detection: the figurative language of social media. Data Knowl Eng 74:1–12. https://doi.org/10.1016/j.datak.2012.02.005CrossRef

Reyes A, Rosso P, Veale T (2013) A multidimensional approach for detecting irony in twitter. Lang Res Eval 47(1):239–268. https://doi.org/10.1007/s10579-012-9196-xCrossRef

Ribeiro MT, Singh S, Guestrin C (2016) “ why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135–1144. https://doi.org/10.1145/2939672.2939778

Rohanian O, Taslimipoor S, Evans R, et al (2018) Wlv at semeval-2018 task 3: Dissecting tweets in search of irony. Association for Computational Linguistics. https://doi.org/10.18653/v1/s18-1090

Ruiz de Mendoza F, Lozano I (2021) On verbal and situational irony: towards a unified approach. https://doi.org/10.1075/ftl.11.07rui

Sennrich R, Haddow B, Birch A (2015) Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909https://doi.org/10.48550/arXiv.1508.07909

Sharma C, Bhageria D, Scott W, et al (2020) Semeval-2020 task 8: Memotion analysis–the visuo-lingual metaphor! arXiv preprint arXiv:2008.03781https://doi.org/10.48550/arXiv.2008.03781

Song B, Pan C, Wang S, et al (2021) Deepblueai at semeval-2021 task 7: Detecting and rating humor and offense with stacking diverse language model-based methods. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1130–1134. https://doi.org/10.18653/v1/2021.semeval-1.158

Sperber D, Wilson D (1981) Irony and the use-mention distinction. Philosophy 3:143–184

Swamy SD, Jamatia A, Gambäck B, et al (2019) Nit_agartala_nlp_team at semeval-2019 task 6: An ensemble approach to identifying and categorizing offensive language in twitter social media corpora. In: NAACL HLT 2019 the international workshop on semantic evaluation proceedings of the thirteenth workshop, Association for Computational Linguistics. https://doi.org/10.18653/v1/s19-2124

Swamy SD, Laddha S, Abdussalam B, et al (2020) Nit-agartala-nlp-team at semeval-2020 task 8: building multimodal classifiers to tackle internet humor. arXiv preprint arXiv:2005.06943https://doi.org/10.48550/arXiv.2005.06943

Tasneem F, Naim J, Chy AN (2020) Harnessing ensemble of data preprocessing and hand-crafted features for irony detection in tweets. In: 2020 23rd international conference on computer and information technology (ICCIT), IEEE, pp 1–6, https://doi.org/10.1109/iccit51783.2020.9392711

Tay Y, Zhang A, Tuan LA, et al (2019) Lightweight and efficient neural natural language processing with quaternion networks. arXiv preprint arXiv:1906.04393https://doi.org/10.48550/arXiv.1906.04393

Tay Y, Dehghani M, Bahri D, et al (2020) Efficient transformers: a survey. arXiv preprint arXiv:2009.06732https://doi.org/10.48550/arXiv.2009.06732

Tellez ES, Miranda-Jiménez S, Graff M, et al (2017) A simple approach to multilingual polarity classification in twitter. Pattern Recogn Lett 94:68–74. https://doi.org/10.48550/arXiv.1612.05270

Tellez ES, Moctezuma D, Miranda-Jiménez S, et al (2018) An automated text categorization framework based on hyperparameter optimization. Knowl Based Syst 149:110–123. https://doi.org/10.48550/arXiv.1704.01975

Tobin V, Israel M (2012) Irony as a viewpoint phenomenon. In: Viewpoint in language: a multimodal perspective, pp 25–46

Tomás D, Ortega-Bueno R, Zhang G, et al (2022) Transformer-based models for multimodal irony detection. J Ambient Intell Human Comput, pp 1–12

Tsipras D, Santurkar S, Engstrom L, et al (2018) Robustness may be at odds with accuracy. arXiv preprint arXiv:1805.12152https://doi.org/10.48550/arXiv.1805.12152

van den Beukel S, Aroyo L (2018) Homonym detection for humor recognition in short text. In: Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 286–291. https://doi.org/10.18653/v1/w18-6242

Van Hee C, Lefever E, Hoste V (2018) Semeval-2018 task 3: Irony detection in english tweets. In: Proceedings of The 12th international workshop on semantic evaluation, pp 39–50. https://doi.org/10.18653/v1/s18-1005

Wang SI, Manning CD (2012) Baselines and bigrams: Simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics (volume 2: Short Papers), pp 90–94. https://aclanthology.org/P12-2018

Weller O, Seppi K (2019) Humor detection: a transformer gets the last laugh. arXiv preprint arXiv:1909.00252https://doi.org/10.48550/arXiv.1909.00252

Wilson D (2006) The pragmatics of verbal irony: Echo or pretence? Lingua 116(10):1722–1743. https://doi.org/10.1016/j.lingua.2006.05.001. (Language in mind: a tribute to Neil Smith on the Occasion of his Retirement)CrossRef

Wilson D, Sperber D (1992) On verbal irony. Lingua 87(1):53–76CrossRef

Wilson D, Sperber D (2012) Explaining irony. Meaning Relevance, pp 123–145

Wu C, Wu F, Wu S, et al (2018) Thu_ngn at semeval-2018 task 3: Tweet irony detection with densely connected lstm and multi-task learning. In: Proceedings of The 12th international workshop on semantic evaluation, pp 51–56. https://doi.org/10.18653/v1/s18-1006

Zampieri M, Malmasi S, Nakov P, et al (2019) Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983https://doi.org/10.48550/arXiv.1903.08983

Zhang M, Yang J, Teng Z, et al (2016) Libn3l: a lightweight package for neural nlp. In: Proceedings of the tenth international conference on language resources and evaluation (LREC’16), pp 225–229. https://aclanthology.org/L16-1034

Zhao X, Xu B, Zheng D, et al (2018) Tweet irony detection using ensembles of word level attentive long short-term memory and convolutional neural network. In: 2018 14th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), IEEE, pp 524–529. https://doi.org/10.1109/fskd.2018.8687128

Zhao Y, Tao X (2021) Zyj at semeval-2021 task 7: Hahackathon: Detecting and rating humor and offense with albert-based model. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1175–1178. https://doi.org/10.18653/v1/2021.semeval-1.165

Zhou C, Sun C, Liu Z, et al (2015) A c-lstm neural network for text classification. arXiv preprint arXiv:1511.08630https://doi.org/10.48550/arXiv.1511.08630

Zylich B, Gugnani A, Brookman G, et al (2021) Amherst685 at semeval-2021 task 7: joint modeling of classification and regression for humor and offense. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021), pp 1190–1195. https://doi.org/10.18653/v1/2021.semeval-1.168

Title: Exploiting stacked embeddings with LSTM for multilingual humor and irony detection
Authors: Radiathun Tasnia
Nabila Ayman
Afrin Sultana
Abu Nowshed Chy
Masaki Aono
Publication date: 01-12-2023
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01049-0

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Exploiting stacked embeddings with LSTM for multilingual humor and irony detection

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