[1] |
SCHAPIRE R E,SINGER Y. Improved boosting algorithms using confidence-rated predictions[M]// Machine Learning. Kluwer Academic Publishers, 1999,37:297-336.
|
[2] |
HE H H,XIA R. Joint binary neural network for multi-label learning with applications to emotion classification[C]// CCF International Conference on Natural Language Processing and Chinese Computing(NLPCC). 2018:250-259.
|
[3] |
CAMRAS L. Emotion: A psychoevolutionary synthesis by Robert Plutchik[J]. The American Journal of Psychology,1980,93(4):751-753.
|
[4] |
BAZIOTIS C,NIKOLAOS A,CHRONOPOULOU A,et al. NTUA-SLP at SemEval-2018 task 1: Predicting affective content in tweets with deep attentive RNNs and transfer learning[C]// Proceedings of the 12th International Workshop on Semantic Evaluation. 2018:245-255.
|
[5] |
FEI H,ZHANG Y,REN Y F,et al. Latent emotion memory for multi-label emotion classification[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2020:7692-7699.
|
[6] |
ALHUZALI H,ANANIADOU S. SpanEmo: Casting multi-label emotion classification as span-prediction[C]// Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. 2021:1573-1584.
|
[7] |
DEVLIN J,CHANG M,LEE K,et al. BERT:Pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of NAACL-HLT. 2019:4171-4186.
|
[8] |
YEH C K,WU W C,KO W J,et al. Learning deep latent space for multi-label classification[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2017,31. DOI:10.1609/aaai.v31i1.10769.
|
[9] |
PANKO R R. Thinking is bad:Implications of human error research for spreadsheet research and practice[C]// Proceedings of European Spreadsheet Risks Interest Group. 2007:69-80.
|
[10] |
DERIU J,LUCCHI A,DE LUCA V,et al. Leveraging large amounts of weakly supervised data for multi-language sentiment classification[C]// Proceedings of the 26th International Conference on World Wide Web. 2017:1045-1052.
|
[11] |
VINCENT P,LAROCHELLE H,LAJOIE I,et al. Stacking denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research,2010,110(11):3371-3408.
|
[12] |
SERGIO G C,LEE M. Stacked DeBERT: All attention in incomplete data for text classification[J]. Neural Networks. 2021,136:87-96.
|
[13] |
罗俊,陈黎飞. 基于BERT的不完全数据情感分类[J]. 计算机应用,2021,41(1):139-144.
|
[14] |
VINCENT P,LAROCHELLE H,BENGIO Y,et al. Extracting and composing robust features with denoising autoencoders[C]// Proceedings of the 25th International Conference on Machine learning. 2008:1096-1103.
|
[15] |
MIKOLOV T,CHEN K,CORRADO G,et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781,2013.
|
[16] |
PETERS M E,NEUMANN M,LAYYER M,et al. Deep contextualized word representations[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2018:2227-2237.
|
[17] |
BAZIOTIS C,PELEKIS N,DOULKERIDIS C. DataStories at SemEval-2017 task 4: Deep LSTM with attention for message-level and topic-based sentiment analysis[C]// Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 2017:747-754.
|
[18] |
MOHAMMAD S,BRAVO-MARQUEZ F,SALAMEH M,et al. SemEval-2018 task 1: Affect in tweets[C]// Proceedings of the 12th International Workshop on Semantic Evaluation. 2018. DOI: 10.18653/v1/S18-1001.
|
[19] |
YU J F,MARUJO L,JIANG J,et al. Improving multi-label emotion classification via sentiment classification with dual attention transfer network[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018:1097-1102.
|
[20] |
ZHOU D Y,YANG Y,HE Y L. Relevant emotion ranking from text constrained with emotion relationships[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2018:561-571.
|
[21] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems. 2011:5998-6008.
|
[22] |
YING W H,XIANG R,LU Q. Improving multi-label emotion classification by integrating both general and domain-specific knowledge[C]// Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT). 2019:316-321.
|
[23] |
XU P,LIU Z H,WINATA G I,et al. Emograph: Capturing emotion correlations using graph networks[J]. arXiv preprint arXiv:2008.09378,2020.
|
[24] |
BADARO G,EL JUNDI O,KHADDAJ A,et al. EMA at semeval-2018 task 1: Emotion mining for Arabic[C]// Proceedings of the 12th International Workshop on Semantic Evaluation. 2018:236-244.
|
[25] |
MULKI H,ALI C B,HADDAD H,et al. Tw-StAR at semeval-2018 task 1: Preprocessing impact on multi-label emotion classification[C]// Proceedings of the 12th International Workshop on Semantic Evaluation. 2018:167-171.
|
[26] |
ALSWAIDAN N,MENAI M E B. Hybrid feature model for emotion recognition in arabic text[J]. IEEE Access. 2020,8:37843-37854.
|
[27] |
GONZALEZ J A,HURTADO L F,PLA F. ELiRF-UPV at semeval-2018 tasks 1 and 3: Affect and irony detection in tweets[C]// Proceedings of the 12th International Workshop on Semantic Evaluation. 2018:565-569.
|