1School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China 2School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China 3Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei 230026, China 4School of Computer Science, Huainan Normal University, Huainan 232038, China
[Objective] This paper explores new algorithms to extract emotion-cause pairs based on multi-label Seq2Seq model. [Methods] First, we used the BERT pre-training to obtain semantically rich word vectors. Then, we utilized the Bi-GRU and LSTM to obtain the global and local features of the texts. Finally, we introduced the hybrid attention mechanism to merge the features and improve the integrity of these semantic features. [Results] Compared with the latest FSS-GCN model, the F1 value of our new model for emotional cause pairs increased by 0.98 percentage point and 11.60 percentage point on two data sets. The F1 value of emotion extraction increased by 0.87 percentage point and 1.10 percentage point, while the F1 value for cause extraction increased by 0.79 percentage point and 2.31 percentage point respectively. [Limitations] Our new model mainly examined the explicit emotion-cause pairs and did not explore implicit emotion-cause pairs. [Conclusions] The proposed model improves the F1 values of extracting emotion-cause pairs.
(Diao Yufeng, Yang Liang, Lin Hongfei, et al. Emotion Cause Recognition Based on Emotion Context Position Attention(ECPA) Neural Network[J]. Journal of Chinese Information Processing, 2021, 35(6): 85-92.)
(Mu Yongli, Li Yang, Wang Suge. Emotion Cause Detection Based on Ensembled Convolution Neural Networks[J]. Journal of Chinese Information Processing, 2018, 32(2): 120-128.)
(Yang Fan, Ye Xiao, Cao Cungen, et al. Acquisition and Analysis of Emotion Knowledge Based on Context and Role[J]. Computer Engineering, 2006, 32(15): 197-199.)
doi: 10.1063/1.30805
[4]
Xia R, Ding Z X. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 1003-1012.
[5]
Cheng Z F, Jiang Z W, Yin Y F, et al. A Unified Target-Oriented Sequence-to-Sequence Model for Emotion-Cause Pair Extraction[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 2779-2791.
doi: 10.1109/TASLP.2021.3102194
[6]
Fan C, Yuan C F, Du J C, et al. Transition-Based Directed Graph Construction for Emotion-Cause Pair Extraction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 3707-3717.
[7]
Poria S, Majumder N, Hazarika D, et al. Recognizing Emotion Cause in Conversations[J]. Cognitive Computation, 2021, 13(5): 1317-1332.
doi: 10.1007/s12559-021-09925-7
[8]
Gao K, Xu H, Wang J. A Rule-Based Approach to Emotion Cause Detection for Chinese Micro-Blogs[J]. Expert Systems with Applications, 2015, 42(9): 4517-4528.
doi: 10.1016/j.eswa.2015.01.064
(Zhang Chen, Qian Tao, Ji Donghong. Joint Model of Microblog Emotion Recognition of Emoticons and Emotion Cause Detection Based on Neural Network[J]. Journal of Computer Applications, 2018, 38(9): 2464-2468.)
doi: 10.11772/j.issn.1001-9081.2018020481
[10]
Xu R F, Hu J N, Lu Q, et al. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMS[J]. Tsinghua Science and Technology, 2017, 22(6): 646-659.
doi: 10.23919/TST.2017.8195347
[11]
Xiao X L, Wei P H, Mao W J, et al. Context-Aware Multi-View Attention Networks for Emotion Cause Extraction[C]// Proceedings of the 2019 IEEE International Conference on Intelligence and Security Informatics. 2019: 128-133.
[12]
Fan C, Yan H Y, Du J C, et al. A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 5614-5624.
[13]
Gui L, Hu J N, He Y L, et al. A Question Answering Approach for Emotion Cause Extraction[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1594-1603.
[14]
Cheng X Y, Chen Y, Cheng B X, et al. An Emotion Cause Corpus for Chinese Microblogs with Multiple-User Structures[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2017, 17(1): 1-19.
[15]
Li X J, Song K S, Feng S, et al. A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4752-4757.
[16]
Li X J, Feng S, Wang D L, et al. Context-Aware Emotion Cause Analysis with Multi-Attention-Based Neural Network[J]. Knowledge-Based Systems, 2019, 174: 205-218.
doi: 10.1016/j.knosys.2019.03.008
[17]
Fan W, Zhu Y X, Wei Z Y, et al. Order-Guided Deep Neural Network for Emotion-Cause Pair Prediction[J]. Applied Soft Computing, 2021, 112: 107818.
doi: 10.1016/j.asoc.2021.107818
[18]
Yu J X, Liu W Y, He Y J, et al. A Mutually Auxiliary Multitask Model with Self-Distillation for Emotion-Cause Pair Extraction[J]. IEEE Access, 2021, 9: 26811-26821.
doi: 10.1109/ACCESS.2021.3057880
[19]
Liu W F, Pang J M, Li N, et al. Research on Multi-Label Text Classification Method Based on tALBERT-CNN[J]. International Journal of Computational Intelligence Systems, 2021, 14(1): 1-12.
doi: 10.2991/ijcis.d.201027.001
(Wang Yuan, Xu Tao, Wang Shilong, et al. An Extreme Multi-Label Text Classification Strategy via Hierarchical Label Semantic Guidance[J]. Journal of Chinese Information Processing, 2021, 35(10): 110-118.)
(Wang Jin, Xu Wei, Ding Yi, et al. Multi-Label Text Classification Based on Graph Embedding and Region Attention[J]. Journal of Jiangsu University(Natural Science Edition), 2022, 43(3): 310-318.)
(Lu Quan, He Chao, Chen Jing, et al. A Multi-Label Classification Model with Two-Stage Transfer Learning[J]. Data Analysis and Knowledge Discovery, 2021, 5(7): 91-100.)
(Lyu Xueqiang, Peng Chen, Zhang Le, et al. Text Multi-Label Classification Method Incorporating BERT and Label Semantic Attention[J]. Journal of Computer Applications, 2022, 42(1): 57-63.)
doi: 10.11772/j.issn.1001-9081.2021020366
[24]
Huang Y, Li Z X, Deng W, et al. D-BERT: Incorporating Dependency-Based Attention into BERT for Relation Extraction[J]. CAAI Transactions on Intelligence Technology, 2021, 6(4): 417-425.
doi: 10.1049/cit2.v6.4
[25]
Zhou P, Shi W, Tian J, et al. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016: 207-212.
[26]
Ding Z X, Xia R, Yu J F. ECPE-2D: Emotion-Cause Pair Extraction Based on Joint Two-Dimensional Representation, Interaction and Prediction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 3161-3170.
[27]
Hu G M, Lu G M, Zhao Y. FSS-GCN: A Graph Convolutional Networks with Fusion of Semantic and Structure for Emotion Cause Analysis[J]. Knowledge-Based Systems, 2021, 212: 106584.
doi: 10.1016/j.knosys.2020.106584
