{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:17:16Z","timestamp":1743139036606,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031171192"},{"type":"electronic","value":"9783031171208"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17120-8_60","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:02:58Z","timestamp":1663938178000},"page":"781-793","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Emotion-Cause Pair Extraction via\u00a0Transformer-Based Interaction Model with\u00a0Text Capsule Network"],"prefix":"10.1007","author":[{"given":"Cheng","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"60_CR1","unstructured":"Chen, Y., Lee, S.Y.M., Li, S., Huang, C.R.: Emotion cause detection with linguistic constructions. In: Proceedings of the 23rd International Conference on Computational Linguistics (Colling 2010), pp. 179\u2013187 (2010)"},{"key":"60_CR2","doi-asserted-by":"crossref","unstructured":"Ding, Z., Xia, R., Yu, J.: 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. 3161\u20133170 (2020)","DOI":"10.18653\/v1\/2020.acl-main.288"},{"key":"60_CR3","doi-asserted-by":"crossref","unstructured":"Fan, C., et al.: A knowledge regularized hierarchical approach for emotion cause analysis. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5614\u20135624 (2019)","DOI":"10.18653\/v1\/D19-1563"},{"key":"60_CR4","doi-asserted-by":"crossref","unstructured":"Fan, C., Yuan, C., Du, J., Gui, L., Yang, M., Xu, R.: Transition-based directed graph construction for emotion-cause pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3707\u20133717 (2020)","DOI":"10.18653\/v1\/2020.acl-main.342"},{"key":"60_CR5","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1109\/TASLP.2021.3089837","volume":"29","author":"C Fan","year":"2021","unstructured":"Fan, C., Yuan, C., Gui, L., Zhang, Y., Xu, R.: Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 2339\u20132350 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"9","key":"60_CR6","doi-asserted-by":"publisher","first-page":"4517","DOI":"10.1016\/j.eswa.2015.01.064","volume":"42","author":"K Gao","year":"2015","unstructured":"Gao, K., Xu, H., Wang, J.: A rule-based approach to emotion cause detection for Chinese micro-blogs. Expert Syst. Appl. 42(9), 4517\u20134528 (2015)","journal-title":"Expert Syst. Appl."},{"key":"60_CR7","doi-asserted-by":"crossref","unstructured":"Gui, L., Xu, R., Wu, D., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: Social Media Content Analysis: Natural Language Processing and Beyond, pp. 145\u2013160. World Scientific (2018)","DOI":"10.1142\/9789813223615_0011"},{"key":"60_CR8","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-662-45924-9_42","volume-title":"Natural Language Processing and Chinese Computing","author":"L Gui","year":"2014","unstructured":"Gui, L., Yuan, L., Xu, R., Liu, B., Lu, Q., Zhou, Yu.: Emotion cause detection with linguistic construction in Chinese Weibo text. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 457\u2013464. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45924-9_42"},{"key":"60_CR9","unstructured":"Lee, S.Y.M., Chen, Y., Huang, C.R.: 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. 45\u201353 (2010)"},{"key":"60_CR10","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.knosys.2019.03.008","volume":"174","author":"X Li","year":"2019","unstructured":"Li, X., Feng, S., Wang, D., Zhang, Y.: Context-aware emotion cause analysis with multi-attention-based neural network. Knowl.-Based Syst. 174, 205\u2013218 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"60_CR11","doi-asserted-by":"crossref","unstructured":"Mohammad, S.M.: Sentiment analysis: detecting valence, emotions, and other affectual states from text. In: Emotion Measurement, pp. 201\u2013237. Elsevier, Amsterdam (2016)","DOI":"10.1016\/B978-0-08-100508-8.00009-6"},{"key":"60_CR12","doi-asserted-by":"crossref","unstructured":"Ou, G., et al.: Exploiting community emotion for microblog event detection. In: Social Media Content Analysis: Natural Language Processing and Beyond, pp. 439\u2013456. World Scientific (2018)","DOI":"10.1142\/9789813223615_0027"},{"key":"60_CR13","doi-asserted-by":"crossref","unstructured":"Qadir, A., Riloff, E.: Learning emotion indicators from tweets: hashtags, hashtag patterns, and phrases. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1203\u20131209 (2014)","DOI":"10.3115\/v1\/D14-1127"},{"key":"60_CR14","unstructured":"Russo, I., Caselli, T., Rubino, F., Boldrini, E., Mart\u00ednez-Barco, P., et al.: EmoCause: an easy-adaptable approach to emotion cause contexts. In: Association for Computational Linguistics (ACL) (2011)"},{"key":"60_CR15","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"60_CR16","unstructured":"Song, H., Zhang, C., Li, Q., Song, D.: End-to-end emotion-cause pair extraction via learning to link. arXiv preprint arXiv:2002.10710 (2020)"},{"key":"60_CR17","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.neucom.2020.03.105","volume":"409","author":"H Tang","year":"2020","unstructured":"Tang, H., Ji, D., Zhou, Q.: Joint multi-level attentional model for emotion detection and emotion-cause pair extraction. Neurocomputing 409, 329\u2013340 (2020)","journal-title":"Neurocomputing"},{"key":"60_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"60_CR19","doi-asserted-by":"crossref","unstructured":"Wei, P., Zhao, J., Mao, W.: 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. 3171\u20133181 (2020)","DOI":"10.18653\/v1\/2020.acl-main.289"},{"key":"60_CR20","doi-asserted-by":"crossref","unstructured":"Xia, R., Ding, Z.: Emotion-cause pair extraction: a new task to emotion analysis in texts. arXiv preprint arXiv:1906.01267 (2019)","DOI":"10.18653\/v1\/P19-1096"},{"key":"60_CR21","doi-asserted-by":"crossref","unstructured":"Xia, R., Zhang, M., Ding, Z.: RTHN: a RNN-transformer hierarchical network for emotion cause extraction. arXiv preprint arXiv:1906.01236 (2019)","DOI":"10.24963\/ijcai.2019\/734"},{"key":"60_CR22","doi-asserted-by":"publisher","first-page":"15573","DOI":"10.1109\/ACCESS.2019.2894701","volume":"7","author":"B Xu","year":"2019","unstructured":"Xu, B., Lin, H., Lin, Y., Diao, Y., Yang, L., Xu, K.: Extracting emotion causes using learning to rank methods from an information retrieval perspective. IEEE Access 7, 15573\u201315583 (2019)","journal-title":"IEEE Access"},{"key":"60_CR23","doi-asserted-by":"crossref","unstructured":"Yada, S., Ikeda, K., Hoashi, K., Kageura, K.: A bootstrap method for automatic rule acquisition on emotion cause extraction. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 414\u2013421. IEEE (2017)","DOI":"10.1109\/ICDMW.2017.60"},{"issue":"2","key":"60_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3057270","volume":"50","author":"A Yadollahi","year":"2017","unstructured":"Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv. (CSUR) 50(2), 1\u201333 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"60_CR25","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.future.2017.09.048","volume":"81","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Wei, Z., Wang, Y., Liao, T.: Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Futur. Gener. Comput. Syst. 81, 395\u2013403 (2018)","journal-title":"Futur. Gener. Comput. Syst."}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17120-8_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:12:30Z","timestamp":1663938750000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17120-8_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171192","9783031171208"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17120-8_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"327","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"73","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}