[1]HU Wenbin,CHEN Long,HUANG Xianbo,et al.A multimodal Chinese sarcasm detection model for emergencies based on cross attention[J].CAAI Transactions on Intelligent Systems,2024,19(2):392-400.[doi:10.11992/tis.202212011]
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A multimodal Chinese sarcasm detection model for emergencies based on cross attention

References:
[1] ZHANG M, ZHANG Y, FU G. Tweet sarcasm detection using deep neural network[C]// International Conference on Computational Linguistics. Osaka: The COLING 2016 Organizing Committee, 2016: 2449-2460.
[2] GHOSH A, VEALE D T. Fracking sarcasm using neural network[C]//Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg: Association for Computational Linguistics, 2016: 161-169.
[3] KHOTIJAH S, TIRTAWANGSA J, SURYANI A A. Using LSTM for context based approach of sarcasm detection in twitter[C]//Proceedings of the 11th International Conference on Advances in Information Technology. New York: ACM, 2020: 1-7.
[4] LOU Chenwei, LIANG Bin, GUI Lin, et al. Affective dependency graph for sarcasm detection[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2021: 1844-1849.
[5] 孙晓, 何家劲, 任福继. 基于多特征融合的混合神经网络模型讽刺语用判别[J]. 中文信息学报, 2016, 30(6): 215–223
SUN Xiao, HE Jiajin, REN Fuji. Pragmatic analysis of irony based on hybrid neural network model with multi-feature[J]. Journal of Chinese information processing, 2016, 30(6): 215–223
[6] 卢欣, 李旸, 王素格. 融合语言特征的卷积神经网络的反讽识别方法[J]. 中文信息学报, 2019, 33(5): 31–38
LU Xin, LI Yang, WANG Suge. Linguistic features enhanced convolutional neural networks for irony recognition[J]. Journal of Chinese information processing, 2019, 33(5): 31–38
[7] 樊小超, 杨亮, 林鸿飞, 等. 基于多语义融合的反讽识别[J]. 中文信息学报, 2021, 35(6): 103–111
FAN Xiaochao, YANG Liang, LIN Hongfei, et al. Irony recognition based on multiple semantic fusion[J]. Journal of Chinese information processing, 2021, 35(6): 103–111
[8] SCHIFANELLA R, DE JUAN P, TETREAULT J, et al. Detecting sarcasm in multimodal social platforms[EB/OL]. (2016-08-08)[2021-01-01]. http://arxiv.org/abs/1608.02289.pdf
[9] SHARMA D K, SINGH B, AGARWAL S, et al. Sarcasm detection over social media platforms using hybrid auto-encoder-based model[J]. Electronics, 2022, 11(18): 2844.
[10] SANGWAN S, AKHTAR M S, BEHERA P, et al. I didn’t mean what I wrote! exploring multimodality for sarcasm detection[C]//2020 International Joint Conference on Neural Networks. Glasgow: IEEE, 2020: 1-8.
[11] CAI Yitao, CAI Huiyu, WAN Xiaojun. Multi-modal sarcasm detection in twitter with hierarchical fusion model[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2019: 2506-2515.
[12] PAN Hongliang, LIN Zheng, FU Peng, et al. Modeling intra and inter-modality incongruity for multi-modal sarcasm detection[C]//Findings of the Association for Computational Linguistics: EMNLP 2020. Online. Stroudsburg: Association for Computational Linguistics, 2020: 1383-1392.
[13] YAO Fanglong, SUN Xian, YU Hongfeng, et al. Mimicking the brain’s cognition of sarcasm from multidisciplines for twitter sarcasm detection[J]. IEEE transactions on neural networks and learning systems, 2023, 34(1): 228–242.
[14] GUPTA S, SHAH A, SHAH M, et al. FiLMing multimodal sarcasm detection with attention[C]//International Conference on Neural Information Processing. Cham: Springer, 2021: 178-186.
[15] 张继东, 蒋丽萍. 基于多模态深度学习的旅游评论反讽识别研究[J]. 情报理论与实践, 2022, 45(7): 158–164
ZHANG Jidong, JIANG Liping. Research on irony recognition of travel reviews based on multi-modal deep learning[J]. Information studies:theory & application, 2022, 45(7): 158–164
[16] 刘洋, 马莉莉, 张雯, 等. 基于跨模态深度学习的旅游评论反讽识别[J]. 数据分析与知识发现, 2022, 6(12): 23–31
LIU Yang, MA Lili, ZHANG Wen, et al. Detecting sarcasm from travel reviews based on cross-modal deep learning[J]. Data analysis and knowledge discovery, 2022, 6(12): 23–31
[17] 刘美萍. 重大突发事件网络舆情协同治理机制构建研究[J]. 求实, 2022(5): 64–76,111
LIU Meiping. Research on mechanism construction of collaborative governance of online public opinion on major emergencies[J]. Truth seeking, 2022(5): 64–76,111
[18] MIKOLOV T, CHEN Kai, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL]. (2013-01-16)[2021-01-01]. http://arxiv.org/abs/1301.3781.pdf.
[19] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1746–1751.
[20] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[21] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems 30. Long Beach, USA, 2017: 5998-6008.
[22] WU Suyan, SU Entong, LEI Binyang, et al. TextCNN-based text classification for E-government[C]//2019 6th International Conference on Information Science and Control Engineering. Shanghai: IEEE, 2020: 929-934.
[23] SHARFUDDIN A A, TIHAMI N M, ISLAM S M. A deep recurrent neural network with BiLSTM model for sentiment classification[C]//2018 International Conference on Bangla Speech and Language Processing . Sylhet: IEEE, 2018: 1-4.
[24] DEVLIN J, CHANG MINGWEI , LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: [s. n. ], 2019: 4171–4186.
[25] 陆晓蕾, 倪斌. 基于预训练语言模型的BERT-CNN多层级专利分类研究[J]. 中文信息学报, 2021, 35(11): 70–79
LU Xiaolei, NI Bin. BERT-CNN: a hierarchical patent classifier based on pre-trained language model[J]. Journal of Chinese information processing, 2021, 35(11): 70–79
[26] LI Zhengguang, LIN Hongfei, SHEN Chen, et al. Cross 2 Self-attentive bidirectional recurrent neural network with BERT for biomedical semantic text similarity[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine. Seoul: IEEE, 2021: 1051-1054.
[27] KAUR T, GANDHI T K. Automated brain image classification based on VGG-16 and transfer learning[C]//2019 International Conference on Information Technology. Bhubaneswar: IEEE, 2020: 94-98.
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