Abstract
Sarcasm detection is a challenging task in sentiment computing since sarcasm belongs to a prevalent and sophisticated linguistic phenomenon. Detecting sarcastic sentiment in utterances can avoid misunderstanding the true intentions of speakers and promote the development of natural language processing. However, existing approaches make effort to design complex model structures, ignoring that knowledge is also crucial for sarcasm recognition. In this paper, we propose a novel Sememe and Syntax Knowledge enhanced Sarcasm Detection (SSK-SD) integrating linguistic knowledge (i.e., sememe knowledge and syntax knowledge), where semantic knowledge captures the potential contradictory emotions between words. In contrast, syntactic knowledge enhances the latent semantic representation of the sentences. Experimental results on two benchmark datasets (i.e., Headlines and SARC) demonstrate that SSK-SD significantly improves the state-of-the-art methods on the Sarcasm Detection task, further indicating that knowledge can enhance Sarcasm Detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Definition by Oxford English Dictionary at https://www.oxfordlearnersdictionaries.com/.
- 2.
We do not distinguish between sarcastic and ironic sentences for convenience.
- 3.
- 4.
- 5.
References
Akula, R., Garibay, I.: Interpretable multi-head self-attention architecture for sarcasm detection in social media (2021)
Amir, S., Wallace, B.C., Lyu, H., Carvalho, P., Silva, M.J.: Modelling context with user embeddings for sarcasm detection in social media (2016)
Babanejad, N., Davoudi, H., An, A., Papagelis, M.: Affective and contextual embedding for sarcasm detection. In: Proceedings of the 28th International Conference on Computational Linguistics (COLING’ 2020) (2020)
Bai, J., Wang, Y., Chen, Y., Yang, Y., Tong, Y.: Syntax-BERT: improving pre-trained transformers with syntax trees (2021)
Barbieri, F., Ronzano, F., Sagigon, H.: Italian irony detection in twitter: a first approach. In: First Italian Conference on Computational Linguistics (2014)
Barbieri, F., Saggion, H.: 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 (2014)
Barbieri, F., Saggion, H., Ronzano, F.: Modelling sarcasm in Twitter, a novel approach. In: Meeting of the Association for Computational Linguistics (2014)
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Simaan, K.: Graph convolutional encoders for syntax-aware neural machine translation (2017)
Bharti, S.K., Babu, K.S., Jena, S.K.: Parsing-based sarcasm sentiment recognition in Twitter data. In: IEEE/ACM International Conference on Advances in Social Networks Analysis Mining, pp. 1373–1380 (2015)
Bloomfield, L.: A set of postulates for the science of language. Language (1926)
Katza, J.D.C.A.N.: Are there necessary conditions for inducing a sense of sarcastic irony? Discourse Process. 49(6), 459–480 (2012)
Chia, Z.L., Ptaszynski, M., Masui, F., Leliwa, G., Wroczynski, M.: Machine learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection. Inf. Process. Manag. Libr. Inf. Retrieval Syst. Commun. Netw. Int. J. (4), 58 (2021)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 6–12 July 2002, Philadelphia, PA, USA (2002)
Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcasm in twitter and Amazon. In: CONLL (2010)
Dong, Z., Dong, Q.: Hownet - a hybrid language and knowledge resource. In: International Conference on Natural Language Processing and Knowledge Engineering (2003)
Dong, Z., Dong, Q., Hao, C.: Hownet and its computation of meaning. Assoc. Comput. Linguist. (2010)
Dong, Z., Dong, Q.: HowNet and The Computation of Meaning (2006)
Duan, S., Zhao, H., Zhou, J., Wang, R.: Syntax-aware transformer encoder for neural machine translation. In: 2019 International Conference on Asian Language Processing (IALP) (2019)
Duan, X., Zhao, J., Xu, B.: Word sense disambiguation through sememe labeling. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence. IJCAI 2007, 6–12 January 2007, Hyderabad, India (2007)
Ghosh, A., Veale, T.: Fracking sarcasm using neural network. In: 7th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (2016)
Ghosh, A., Veale, T.: Magnets for sarcasm: making sarcasm detection timely, contextual and very personal. In: Empirical Methods in Natural Language Processing 2017 (2017)
Gu, Y., Yan, J., Zhu, H., Liu, Z.: Language modeling with sparse product of sememe experts (2018)
Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R., Mihalcea, R.: Cascade: contextual sarcasm detection in online discussion forums (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ili, S., Marrese-Taylor, E., Balazs, J.A., Matsuo, Y.: Deep contextualized word representations for detecting sarcasm and irony (2018)
Joshi, A., Patel, K., Tripathi, V., Bhattacharyya, P., Carman, M.: Are word embedding-based features useful for sarcasm detection?’ (2016)
Khodak, M., Saunshi, N., Vodrahalli, K.: A large self-annotated corpus for sarcasm (2017)
Kim, Y.: Convolutional neural networks for sentence classification. ePrint arXiv (2014)
Kiperwasser, E., Ballesteros, M.: Scheduled multi-task learning: from syntax to translation. Trans. Assoc. Comput. Linguist. 6(2) (2018)
Liu, L., Priestley, J.L., Zhou, Y., Ray, H.E., Han, M.: A2Text-net: a novel deep neural network for sarcasm detection. In: The 2019 IEEE International Conference on Cognitive Machine Intelligence (2019)
Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Mcclosky, D.: The Stanford coreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (2014)
Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling (2017)
Maynard, D.G., Greenwood, M.A.: Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: Language Resources and Evaluation Conference (LREC) (2014)
Misra, R., Arora, P.: Sarcasm detection using hybrid neural network (2019)
Niu, Y., Xie, R., Liu, Z., Sun, M.: Improved word representation learning with sememes (2017)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation (2014)
Qi, F., et al.: Modeling semantic compositionality with sememe knowledge (2019)
Fanchao, Q.I., Xie, R., Zang, Y., Liu, Z., Sun, M.: Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases (2021)
Qi, F., Yang, C., Liu, Z., Dong, Q., Dong, Z.: Openhownet: an open sememe-based lexical knowledge base (2019)
Qin, S., Rong, W., Shi, L., Yang, J., Xiong, Z.: Syntax tree aware adversarial question rewriting for answer selection. In: 2019 International Joint Conference on Neural Networks (IJCNN) (2019)
Qin, Y., Qi, F., Ouyang, S., Liu, Z., Sun, M.: Improving sequence modeling ability of recurrent neural networks via sememes. IEEE/ACM Trans. Audio Speech Lang. Process. 1 (2020)
Ren, L., Xu, B., Lin, H., Liu, X., Yang, L.: Sarcasm detection with sentiment semantics enhanced multi-level memory network. Neurocomputing 401 (2020)
Rosso, R.P.: Making objective decisions from subjective data: detecting irony in customer reviews. Decis. Support Syst. (2012)
Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)
Antonio, R., Paolo, R., Tony, V.: A multidimensional approach for detecting irony in Twitter. Lang. Resourc. Eval. (2013)
Riloff, E., Qadir, A., Surve, P., Silva, L.D., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation (2013)
Roth, M., Lapata, M.: Neural semantic role labeling with dependency path embeddings (2016)
Sachan, D.S., Zhang, Y., Qi, P., Hamilton, W.L.: Do syntax trees help pre-trained transformers extract information?. In: Conference of the European Chapter of the Association for Computational Linguistics (2021)
Sandra, D., Taft, M.: Morphological structure, lexical representation and lexical access. Am. J. Psychol. 111(3), 445 (2013)
Shen, Y., Tan, S., Sordoni, A., Courville, A.: Ordered neurons: integrating tree structures into recurrent neural networks (2018)
Shi, H., Zhou, H., Chen, J., Li, L.: On tree-based neural sentence modeling (2018)
Shmueli, B., Ku, L.W., Ray, S.: Reactive supervision: a new method for collecting sarcasm data (2020)
Tay, Y., Tuan, L.A., Hui, S.C., Su, J.: Reasoning with sarcasm by reading in-between (2018)
Wen, Z., et al.: Sememe knowledge and auxiliary information enhanced approach for sarcasm detection. Inf. Process. Manag. Libr. Inf. Retrieval Syst. Commun. Netw. Int. J. (3), 59 (2022)
Wierzbicka, A.: Semantics: primes and universals. Language (1996)
Wu, H., Liu, Y., Shi, S.: Modularized syntactic neural networks for sentence classification. In: Empirical Methods in Natural Language Processing (2020)
Wu, H., Zhang, Z., Song, H., Shi, S., Wu, Q.: Phrase dependency relational graph attention network for aspect-based sentiment analysis. Knowl.-Based Syst. (Jan.25), 236 (2022)
Xiong, T., Zhang, P., Zhu, H., Yang, Y.: Sarcasm detection with self-matching networks and low-rank bilinear pooling (2019)
Zanzotto, F.M., Santilli, A.: Syntnn at SemEval-2018 task 2: is syntax useful for emoji prediction? Embedding syntactic trees in multi layer perceptrons. In: North American Chapter of the Association for Computational Linguistics (2018)
Zhang, M., Zhang, Y., Fu, G.: Tweet sarcasm detection using deep neural network. In: International Conference on Computational Linguistics (2016)
Zhang, S., Zhang, X., Chan, J., Rosso, P.: Irony detection via sentiment-based transfer learning. Inf. Process. Manag. 56(5), 1633–1644 (2019)
Zhang, X., Chen, Y., Li, G.: Multi-modal sarcasm detection based on contrastive attention mechanism. In: International Conference Natural Language Processing (2021)
Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant (61972336, 62306267), and Zhejiang Provincial Natural Science Foundation of China under Grant (LY23F020001, LY22F020027).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Z., Shan, J., Wu, H., Chen, Y., Jiang, J., Wang, W. (2024). Deep Sarcasm Detection with Sememe and Syntax Knowledge. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14964. Springer, Singapore. https://doi.org/10.1007/978-981-97-7241-4_26
Download citation
DOI: https://doi.org/10.1007/978-981-97-7241-4_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-7240-7
Online ISBN: 978-981-97-7241-4
eBook Packages: Computer ScienceComputer Science (R0)