{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:56:40Z","timestamp":1772452600525,"version":"3.50.1"},"reference-count":53,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100020569","name":"Hong Kong Metropolitan University","doi-asserted-by":"publisher","award":["CP\/2022\/02"],"award-info":[{"award-number":["CP\/2022\/02"]}],"id":[{"id":"10.13039\/100020569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["UGC\/FDS16\/E10\/23"],"award-info":[{"award-number":["UGC\/FDS16\/E10\/23"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014736","name":"Lingnan University","doi-asserted-by":"publisher","award":["SDS24A2"],"award-info":[{"award-number":["SDS24A2"]}],"id":[{"id":"10.13039\/501100014736","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>Recent studies have extensively explored the causal connections between emotions and their underlying causes in textual data. Most research aims to identify clauses within documents that are causally related. However, these studies have overlooked the fact that such causal relationships are often context-dependent and valid only within specific contextual clauses. To bridge this gap, we present a novel task of determining the presence of a valid causal relationship between a given pair of emotion and cause clauses in different contexts, while also identifying the specific contextual clauses involved. Since this task is novel and lacks an existing dataset for testing, we manually annotate a benchmark dataset to obtain labels for our task and classify the types of context clauses, which can also be beneficial for other applications. By leveraging negative sampling, we create a balanced final dataset that includes documents with and without causal relationships. Building upon this dataset, we propose an end-to-end multi-task framework that incorporates two innovative modules aimed at achieving the objectives of our task. We introduce a context masking module to identify the contextual clauses that contribute to causal relationships and a prediction aggregation module to refine predictions by determining the reliance of emotion and cause clauses on specific contextual clauses. Extensive comparative experiments and ablation studies validate the effectiveness and robustness of our proposed framework. The annotated dataset provides a novel way for exploring complex reasoning in causal analysis.<\/jats:p>","DOI":"10.1177\/24056456251371923","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T14:31:27Z","timestamp":1756909887000},"page":"579-595","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions"],"prefix":"10.1177","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8563-148X","authenticated-orcid":false,"given":"Xinhong","family":"Chen","sequence":"first","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1708-7099","authenticated-orcid":false,"given":"Zongxi","family":"Li","sequence":"additional","affiliation":[{"name":"Lingnan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[{"name":"Lingnan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-7622","authenticated-orcid":false,"given":"Kevin","family":"Hung","sequence":"additional","affiliation":[{"name":"Hong Kong Metropolitan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2024.3419593"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00273171.2011.568786"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.08.005"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021316409277"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Cambria E. Li Y. Xing F. Z. Poria S. Kwok K. (2020). Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM International conference on information & knowledge management (p. 105\u2013114). CIKM \u201920. New York NY USA: Association for Computing Machinery. ISBN 9781450368599.","DOI":"10.1145\/3340531.3412003"},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Cambria E. Zhang X. Mao R. Chen M. Kwok K. (2024). Senticnet 8: Fusing emotion ai and commonsense ai for interpretable trustworthy and explainable affective computing. In: International conference on human-computer interaction (pp.\u00a0197\u2013216). Springer.","DOI":"10.1007\/978-3-031-76827-9_11"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma15010354"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.125437"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3045812"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3218648"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Chen X. Li Q. Wang J. (2020a). Conditional causal relationships between emotions and causes in texts. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp.\u00a03111\u20133121). Online: Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.emnlp-main.252"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Chen X. Li Q. Wang J. (2020b). A unified sequence labeling model for emotion cause pair extraction. In: Proceedings of the 28th international conference on computational linguistics (pp.\u00a0208\u2013218). Barcelona Spain (Online): International Committee on Computational Linguistics.","DOI":"10.18653\/v1\/2020.coling-main.18"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nlp.2025.100125"},{"key":"e_1_3_3_15_1","doi-asserted-by":"crossref","unstructured":"Chen Y. Hou W. Cheng X. Li S. (2018). Joint learning for emotion classification and emotion cause detection. In: Proceedings of the 2018 conference on empirical methods in natural language processing (pp.\u00a0646\u2013651). Brussels Belgium: Association for Computational Linguistics.","DOI":"10.18653\/v1\/D18-1066"},{"key":"e_1_3_3_16_1","unstructured":"Chen Y. Lee S. Y. M. Li S. Huang C. R. (2010). Emotion cause detection with linguistic constructions. In: Proceedings of the 23rd international conference on computational linguistics (pp.\u00a0179\u2013187). COLING \u201910."},{"key":"e_1_3_3_17_1","doi-asserted-by":"crossref","unstructured":"Ding Z. He H. Zhang M. Xia R. (2019). From independent prediction to reordered prediction: Integrating relative position and global label information to emotion cause identification. In: The Thirty-Third AAAI conference on artificial intelligence AAAI 2019 (pp.\u00a06343\u20136350).","DOI":"10.1609\/aaai.v33i01.33016343"},{"key":"e_1_3_3_18_1","doi-asserted-by":"crossref","unstructured":"Ding Z. Xia R. Yu J. (2020). Ecpe-2d: Emotion-cause pair extraction based on joint two-dimensional representation interaction and prediction. In: Proceedings of the 58th annual meeting of the association for computational linguistics (pp.\u00a03161\u20133170).","DOI":"10.18653\/v1\/2020.acl-main.288"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649451"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.1993.10474623"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Gui L. Wu D. Xu R. Lu Q. Zhou Y. (2016a). Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 conference on empirical methods in natural language processing (pp.\u00a01639\u20131649).","DOI":"10.18653\/v1\/D16-1170"},{"key":"e_1_3_3_22_1","doi-asserted-by":"crossref","unstructured":"Gui L. Xu R. Lu Q. Wu D. Zhou Y. (2016b). Emotion cause extraction a challenging task with corpus construction. In: Li Y. Xiang G. Lin H. & Wang M. (Eds.) Social media processing (pp.\u00a098\u2013109).","DOI":"10.1007\/978-981-10-2993-6_8"},{"key":"e_1_3_3_23_1","unstructured":"Guo R. Cheng L. Li J. Hahn P. R. Liu H. (2018). A survey of learning causality with data: Problems and methods. Computing Research Repository arXiv:1809.09337. Version 3."},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2023.3250833"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465304323023651"},{"key":"e_1_3_3_26_1","unstructured":"Jiang A. Q. Sablayrolles A. Mensch A. Bamford C. Chaplot D. S. Casas Ddl Bressand F. Lengyel G. Lample G. Saulnier L. et\u00a0al. (2023). Mistral 7b. arXiv preprint arXiv:2310.06825."},{"key":"e_1_3_3_27_1","first-page":"578","article-title":"Pragmatics and computational linguistics","author":"Jurafsky D.","year":"2004","unstructured":"Jurafsky D. (2004). Pragmatics and computational linguistics. The Handbook of Pragmatics 578\u2013604.","journal-title":"The Handbook of Pragmatics"},{"key":"e_1_3_3_28_1","unstructured":"Kayesh H. Islam M. S. Wang J. (2019). On event causality detection in tweets. Computing Research Repository arXiv:1901.03526."},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Kruengkrai C. Torisawa K. Hashimoto C. Kloetzer J. Oh J. Tanaka M. (2017). Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks. In: Singh S. P. & Markovitch S. (Eds.) Proceedings of the Thirty-First AAAI conference on artificial intelligence February 4-9 2017 San Francisco California USA (pp.\u00a03466\u20133473). AAAI Press.","DOI":"10.1609\/aaai.v31i1.11005"},{"key":"e_1_3_3_30_1","unstructured":"Lee S. Y. M. Chen Y. Huang C. R. (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 (pp.\u00a045\u201353)."},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.08.009"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-021-00957-5"},{"key":"e_1_3_3_33_1","unstructured":"Li Z. Li X. Liu Y. Xie H. Li J. Wang F. l. Li Q. Zhong X. (2023). Label supervised llama finetuning. arXiv preprint arXiv:2310.01208."},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107163"},{"issue":"7","key":"e_1_3_3_35_1","first-page":"3188","article-title":"Causal inference on multidimensional data using free probability theory","volume":"29","author":"Liu F.","year":"2018","unstructured":"Liu F., Chan L. W. (2018). Causal inference on multidimensional data using free probability theory. IEEE Transactions on Neural Networks and Learning Systems, 29(7), 3188\u20133198. https:\/\/doi.org\/10.1109\/TNNLS.2017.2716539","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2019.2903056"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1903"},{"key":"e_1_3_3_38_1","unstructured":"Mikolov T. Sutskever I. Chen K. Corrado G. Dean J. (2013). Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th international conference on neural information processing systems - Volume 2 (pp.\u00a03111\u20133119). NIPS\u201913."},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3439726"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Nguyen H. H. Nguyen M. T. (2023). Emotion-cause pair extraction as question answering. arXiv preprint arXiv:2301.01982.","DOI":"10.5220\/0011883100003393"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/82.4.702"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2022.3228129"},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2019.2901673"},{"key":"e_1_3_3_44_1","doi-asserted-by":"crossref","unstructured":"Song H. Zhang C. Li Q. Song D. (2020). An end-to-end multi-task learning to link framework for emotion-cause pair extraction. Computing Research Repository arXiv:2002.10710.","DOI":"10.1117\/12.2607175"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.105"},{"key":"e_1_3_3_46_1","unstructured":"Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A. N. Kaiser L. Polosukhin I. (2017). Attention is all you need. In: Guyon I. Luxburg U. V. Bengio S. Wallach H. Fergus R. Vishwanathan S. & Garnett R. (Eds.) Advances in neural information processing systems 30 (pp.\u00a05998\u20136008). Curran Associates Inc."},{"key":"e_1_3_3_47_1","doi-asserted-by":"crossref","unstructured":"Wei P. Zhao J. Mao W. (2020). Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics (pp.\u00a03171\u20133181). Online: Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.289"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2015.01593"},{"key":"e_1_3_3_49_1","doi-asserted-by":"crossref","unstructured":"Xia R. Ding Z. (2019). Emotion-cause pair extraction: A new task to emotion analysis in texts. In: Proceedings of the 57th annual meeting of the association for computational linguistics (pp.\u00a01003\u20131012).","DOI":"10.18653\/v1\/P19-1096"},{"key":"e_1_3_3_50_1","doi-asserted-by":"crossref","unstructured":"Xia R. Zhang M. Ding Z. (2019). Rthn: A rnn-transformer hierarchical network for emotion cause extraction. In: Proceedings of the 28th international joint conference on artificial intelligence (pp.\u00a05285\u20135291). IJCAI\u201919. ISBN 978-0-9992411-4-1.","DOI":"10.24963\/ijcai.2019\/734"},{"key":"e_1_3_3_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2921613"},{"key":"e_1_3_3_52_1","unstructured":"Yang A. Yang B. Hui B. et\u00a0al. (2024). Qwen2 technical report. arXiv preprint arXiv:2407.10671."},{"key":"e_1_3_3_53_1","unstructured":"You Y. Li J. Reddi S. Hseu J. Kumar S. Bhojanapalli S. Song X. Demmel J. Keutzer K. Hsieh C. J. (2020). Large batch optimization for deep learning: Training bert in 76 minutes. Computing Research Repository arXiv:1904.00962."},{"key":"e_1_3_3_54_1","doi-asserted-by":"crossref","unstructured":"Zhang X. Mao R. Cambria E. (2024). Senticvec: Toward robust and human-centric neurosymbolic sentiment analysis. ACL Findings.","DOI":"10.18653\/v1\/2024.findings-acl.289"}],"container-title":["Web Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/24056456251371923","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/24056456251371923","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/24056456251371923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:00:01Z","timestamp":1772449201000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.1177\/24056456251371923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1177\/24056456251371923"],"URL":"https:\/\/doi.org\/10.1177\/24056456251371923","relation":{},"ISSN":["2405-6456","2405-6464"],"issn-type":[{"value":"2405-6456","type":"print"},{"value":"2405-6464","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]}}}