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Emotion-cause span extraction: a new task to emotion cause identification in texts

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Abstract

Emotional cause identification (ECI) is an important task for emotion analysis, aiming to identify the causes behind a certain emotion expressed in the text. Most of the previous studies are restricted to the clause-level binary classification, which have ignored an important fact that not all words in the clause are useful cause information for people to express emotions. In this work, we propose a new task: emotion-cause span extraction (ECSE), which is capable of obtaining more accurate and effective emotion causes. Inspired by recent advances in using joint learning approaches to ECI, we propose a novel joint learning framework for emotion-cause span extraction and span-based emotion classification so as to better address the ECSE task. Taken as the default backbone network, the Bidirectional Encoder Representations from Transformers (BERT) is used to encode multiple words and serve contextualized token representations. Furthermore, we also propose a multi-attention mechanism with emotional context awareness and a relative position learning mechanism on word-level, which is able to further capture the mutual interactions between the emotion clauses and candidate spans. According to the experimental results on a benchmark emotion cause corpus, it proves the reliability of the ECSE task and the effectiveness of our approach. In addition, through an in-depth analysis of traditional ECI task by converting ECSE into the clause-level binary classification task, we achieve the best performance among the systems in comparison, which further demonstrates the feasibility of the new ECSE task.

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Notes

  1. The corpus was published by Gui [1] in 2017

  2. ELMO model is adapted from this implementation: https://github.com/HIT-SCIR/ELMoForManyLangshttps://github.com/HIT-SCIR/ELMoForManyLangs. In ECSE-ELMO method, we set the embedding dimensionality to be d = 768. The initial learning rate is 105 and the batch size is 32

  3. GPT model is adapted from this implementation: https://github.com/openai/gpt-2. In ECSE-GPT method, we use the learned embeddings with supported sequence lengths of up to 512 tokens to be consistent with our experimental setting. A batch size of 8, learning rate is 5e-5 and classifier dropout with a rate of 0.1

References

  1. Gui L, Wu D, Xu R et al (2016) Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1639–1649

  2. Cheng X, Chen Y, Cheng B et al (2017) An emotion cause corpus for chinese microblogs with multiple-user structures, vol 17

  3. Yada S, Ikeda K, Hoashi K et al (2017) A bootstrap method for automatic rule acquisition on emotion cause extraction. In: 2017 IEEE international conference on data mining workshops (ICDMW), IEEE, pp 414–421

  4. Ding Z, He H, Zhang M et al (2019) From independent prediction to reordered prediction: Integrating relative position and global label information to emotion cause identification. In: Proceedings of the AAAI conference on artificial intelligence (AAAI)

  5. Chen Y, Hou W, Cheng X et al (2018) Joint learning for emotion classification and emotion cause detection. In: Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP), pp 646–651

  6. Xu R, Hu J, Lu Q, at al (2017) An ensemble approach for emotion cause detection with event extraction and multi-kernel svms. Tsinghua Sci Technol 22(6):646–659

    Article  Google Scholar 

  7. Lee S, Y M, Chen Y, Huang CR, at al (2013) Detecting emotion causes with a linguistic rulebased approach, vol 29

  8. Li W, Xu H (2014) Text-based emotion classification using emotion cause extraction. Expert Syst Appl 41(4):1742–1749

    Article  Google Scholar 

  9. Ghazi D, Inkpen D, Szpakowicz S (2015) Detecting emotion stimuli in emotion-bearing sentences. In: International conference on intelligent text processing and computational linguistics, Springer, pp 152–165

  10. Gui L, Hu J, He Y, Xu R, Lu Q, Du J (2017) A question answering approach to emotion cause extraction. In: Empirical methods in natural language processing (EMNLP), pp 1593–1602

  11. Chen Y, Hou W, Cheng X et al (2018) Hierarchical convolution neural network for emotion cause detection on microblogs. In: International conference on artificial neural networks (ICANN), pp 115–122

  12. Xu B, Lin H, Li Y et al (2019) Extracting emotion causes using learning to rank methods from an information retrieval perspective, vol 7

  13. Xia R, Ding Z (2019) Emotion-cause pair extraction: A new task to emotion analysis in texts. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Vol 1: Long Papers, pp 1003–1012

  14. Fan C, Yuan C, Du J et al (2020) Transition-based directed graph construction for emotion-cause pair extraction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

  15. Gao W, Li S, Lee S, at al (2013) Joint learning on sentiment and emotion classification. In: Proceedings of the 22nd ACM international conference on information & knowledge management, pp 1505–1508

  16. Wang R, Li S, Zhou D, at al (2015) Joint sentiment and emotion classification with integer linear programming. In: Liu A, Ishikawa Y, Qian T, Nutanong S, Cheema M (eds) Database systems for advanced applications, lecture notes in computer science, Vol 9052

  17. You Q (2016) Sentiment and emotion analysis for social multimedia: methodologies and Applications[C]// the 2016 ACM ACM

  18. Pavitra R, Kalaivaani PCD (2015) Weakly supervised sentiment analysis using joint sentiment topic detection with bigrams[C]// 2015 2nd International Conference on Electronics and Communication Systems (ICECS) IEEE

  19. Huang F, Zhang S, Zhang J et al Multimodal learning for topic sentiment analysis in microblogging[J]. Neurocomputing, 2017:S0925231217304393

  20. Li W, Xu H (2014) Text-based emotion classification using emotion cause extraction. Expert Syst Appl 41(4):1742–1749

    Article  Google Scholar 

  21. Gao K, Xu H, Wang J (2015) Emotion cause detection for chinese micro-blogs based on ecocc model. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, pp 3–14

  22. Li J, Song S, Feng S, Wang L, at al (2018) A co-attention neural network model for emotion cause analysis with emotional context awareness. In: Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP), pp 4752–4757

  23. Devlin J, Chang W, Lee T, at al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805

  24. Xia R, Zhang M, Ding Z (2019) Rthn: a rnn-transformer hierarchical network for emotion cause extraction

  25. Vaswani A, Shazeer N, Parmar N (2017) Attention is all you need. Inproceedings of NIPS

  26. Zhang H, Qi P, D C (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP)

  27. Hu M, Peng Y, Huang Z et al (2019) Open-domain targeted sentiment analysis via span-based extraction and classification

  28. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473

  29. Peters M, Neumann M, Iyyer M (2018) Deep Contextualized Word Representations[J]

  30. Alec R, Jeffrey W, Rewon C, David L et al (2019) Language models are unsupervised multitask learners. Technical report, OpenAI

  31. Lee S Y M, Chen Y, Huang CR (2010) A text-driven rule-based system for emotion cause detection. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 45–53. Association for Computational Linguistics

  32. Russo I, Caselli T, Rubino F, et al. (2011) EMOCause: an easy-adaptable approach to emotion cause contexts[C]// Workshop on Computational Approaches to Subjectivity & Sentiment Analysis. Association for Computational Linguistics

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant number 61561047); Xin Jiang education funds project of china (grant number 90390007); Key Laboratory project of Xinjiang Normal University, China (grant number XJNUSYS102018B04).

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China

    Min Li, Hui Zhao & Ping Li

  2. Key Laboratory of signal detection and processing in Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China

    Min Li, Hui Zhao & YuRong Qian

  3. Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China

    Min Li & Hui Zhao

  4. School of Software, Xinjiang University, Urumqi, Xinjiang, China

    YuRong Qian

  5. Xinjiang Radio and television Investment Co., Ltd, Urumqi, Xinjiang, China

    Hao Su

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  1. Min Li
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  2. Hui Zhao
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  3. Hao Su
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  4. YuRong Qian
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  5. Ping Li
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Correspondence to Hui Zhao.

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Li, M., Zhao, H., Su, H. et al. Emotion-cause span extraction: a new task to emotion cause identification in texts. Appl Intell 51, 7109–7121 (2021). https://doi.org/10.1007/s10489-021-02188-7

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