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Emotion-cause pair extraction via knowledge-driven multi-classification and graph-based position embedding

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Abstract

Emotion-Cause Pair Extraction (ECPE) is an important yet challenging task, focused on concurrently extracting emotional clauses and their corresponding causal clauses. Despite notable improvements in extraction performance over the past few years, three critical issues have been overlooked: (1) The binary classification of emotional and causal clauses may neglect the emotional semantics. In reality, there exist four types of clauses: emotion, cause, emotion-cause, and none-emotion-cause. (2) The integration of prior knowledge concerning sentiment information and causal information has been lacking. (3) The positional information of emotion-cause pairs may be adversely affected by imprecise emotional hypotheses and unbalanced document lengths. To tackle these challenges, we propose a new knowledge-driven multi-classification sub-task aimed at classifying clauses into the four mentioned types. Additionally, we introduce graph-based position embedding to capture relevant positional information. Experimental results underscore the effectiveness of our approach in addressing these issues.

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Data availability and access

In our study, we utilized publicly available anonymized datasets to conduct our research. The data can be obtained at https://github.com/NUSTM/ECPE

Notes

  1. http://static.bosonnlp.com/dev/resource

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Acknowledgements

This work was supported in part by the Ministry of Education Humanities and Social Science Project under Grant 22YJC740110; in part by the Social Science Planning Foundation of Liaoning Province under Grant L21CXW003.

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, No.321, Tuqiang Street, Development Zone, Dalian, 116620, Liaoning, China

    Linlin Zong, Jinglin Zhang, Jiahui Zhou & Xianchao Zhang

  2. School of Computer Science and Technology, Dalian University of Technology, No. 2 Lingong Road, Ganjingzi District, Dalian, 116024, Liaoning, China

    Bo Xu

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Contributions

Linlin Zong: Conceptualization, Methodology, Funding acquisition, Writing - Review & Editing, Formal analysis Jinglin Zhang: Methodology, Writing - Original Draft Jiahui Zhou: Methodology, Writing - Original Draft Xianchao Zhang: Supervision, Resources Bo Xu: Project administration, Conceptualization, Writing - Review & Editing

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Correspondence to Bo Xu.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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In our study, we utilized publicly available anonymized datasets to conduct our research. As a result, no additional ethical considerations were deemed necessary.

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Zong, L., Zhang, J., Zhou, J. et al. Emotion-cause pair extraction via knowledge-driven multi-classification and graph-based position embedding. Appl Intell 54, 2703–2715 (2024). https://doi.org/10.1007/s10489-024-05326-z

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