Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection-Reference-Cited by-同舟云学术

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Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection

Published:2023-06-28 Issue:12 Volume:14 Page:4119-4134
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Wen Zhiyuan^ORCID,Wang Rui,Luo Xuan,Wang Qianlong,Liang Bin,Du Jiachen,Yu Xiaoqi,Gui Lin,Xu Ruifeng

Funder

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Shenzhen Foundational Research Funding

Shenzhen Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01884-9.pdf

Reference84 articles.

1. Attardo S (2000) Irony as relevant inappropriateness[J]. J Pragmatics 32(6):793–826

2. Gibbs RW (2000) Irony in talk among friends. Metaphor Symbol 15(1–2):5–27

3. Gibbs RW (1986) On the psycholinguistics of sarcasm. J Exp Psychol Gen 115(1):3

4. 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. Association for Computational Linguistics, Beijing, China. https://doi.org/10.3115/v1/P15-2124. https://aclanthology.org/P15-2124

5. Chen I-H, Long Y, Lu Q, Huang C-R (2017) Leveraging eventive information for better metaphor detection and classification. In: Proceedings of the 21st conference on computational natural language learning (CoNLL 2017), pp 36–46

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