{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T22:54:11Z","timestamp":1753052051820,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922375"},{"type":"electronic","value":"9783030922382"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-92238-2_39","type":"book-chapter","created":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T22:02:35Z","timestamp":1638655355000},"page":"470-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DA-GCN: A Dependency-Aware Graph Convolutional Network for Emotion Recognition in Conversations"],"prefix":"10.1007","author":[{"given":"Yunhe","family":"Xie","sequence":"first","affiliation":[]},{"given":"Chengjie","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Bingquan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhenzhou","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"issue":"8","key":"39_CR1","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.1007\/s10115-020-01449-0","volume":"62","author":"N Alswaidan","year":"2020","unstructured":"Alswaidan, N., Menai, M.E.B.: A survey of state-of-the-art approaches for emotion recognition in text. Knowl. Inf. Syst. 62(8), 2937\u20132987 (2020)","journal-title":"Knowl. Inf. Syst."},{"issue":"2","key":"39_CR2","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TCE.2018.2844736","volume":"64","author":"D Ayata","year":"2018","unstructured":"Ayata, D., Yaslan, Y., Kamasak, M.E.: Emotion based music recommendation system using wearable physiological sensors. IEEE Trans. Consum. Electron. 64(2), 196\u2013203 (2018)","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"4","key":"39_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso, C., et al.: IEMOCAP: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42(4), 335\u2013359 (2008). https:\/\/doi.org\/10.1007\/s10579-008-9076-6","journal-title":"Lang. Resour. Eval."},{"issue":"7","key":"39_CR4","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1002\/ajmg.b.32558","volume":"174","author":"JR Coleman","year":"2017","unstructured":"Coleman, J.R., Lester, K.J., Keers, R., Munaf\u00f2, M.R., Breen, G., Eley, T.C.: Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders. Am. J. Med. Genet. B Neuropsychiatr. Genet. 174(7), 701\u2013711 (2017)","journal-title":"Am. J. Med. Genet. B Neuropsychiatr. Genet."},{"key":"39_CR5","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Majumder, N., Poria, S., Chhaya, N., Gelbukh, A.: DialogueGCN: a graph convolutional neural network for emotion recognition in conversation. 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. 154\u2013164 (2019)","DOI":"10.18653\/v1\/D19-1015"},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Gu, Y., et al.: Human conversation analysis using attentive multimodal networks with hierarchical encoder-decoder. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 537\u2013545 (2018)","DOI":"10.1145\/3240508.3240714"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Gu, Y., et al.: Mutual correlation attentive factors in dyadic fusion networks for speech emotion recognition. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 157\u2013166 (2019)","DOI":"10.1145\/3343031.3351039"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Hazarika, D., Poria, S., Zadeh, A., Cambria, E., Morency, L.P., Zimmermann, R.: Conversational memory network for emotion recognition in dyadic dialogue videos. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2122\u20132132 (2018)","DOI":"10.18653\/v1\/N18-1193"},{"key":"39_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2020.06.005","volume":"65","author":"D Hazarika","year":"2021","unstructured":"Hazarika, D., Poria, S., Zimmermann, R., Mihalcea, R.: Conversational transfer learning for emotion recognition. Inf. Fusion 65, 1\u201312 (2021)","journal-title":"Inf. Fusion"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Jiao, W., Lyu, M., King, I.: Exploiting unsupervised data for emotion recognition in conversations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 4839\u20134846 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.435"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Jiao, W., Lyu, M., King, I.: Real-time emotion recognition via attention gated hierarchical memory network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8002\u20138009 (2020)","DOI":"10.1609\/aaai.v34i05.6309"},{"key":"39_CR12","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.dss.2018.09.002","volume":"115","author":"B Kratzwald","year":"2018","unstructured":"Kratzwald, B., Ili\u0107, S., Kraus, M., Feuerriegel, S., Prendinger, H.: Deep learning for affective computing: text-based emotion recognition in decision support. Decis. Support Syst. 115, 24\u201335 (2018)","journal-title":"Decis. Support Syst."},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., Fei, H., Ji, D.: Modeling local contexts for joint dialogue act recognition and sentiment classification with Bi-channel dynamic convolutions. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 616\u2013626 (2020)","DOI":"10.18653\/v1\/2020.coling-main.53"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., Gkoumas, D., Sordoni, A., Nie, J.Y., Melucci, M.: Quantum-inspired neural network for conversational emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 13270\u201313278 (2021)","DOI":"10.1609\/aaai.v35i15.17567"},{"key":"39_CR15","unstructured":"Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: DailyDialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 986\u2013995 (2017)"},{"key":"39_CR16","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TASLP.2021.3049898","volume":"29","author":"Z Lian","year":"2021","unstructured":"Lian, Z., Liu, B., Tao, J.: CTNet: conversational transformer network for emotion recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 985\u20131000 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: DialogueRNN: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6818\u20136825 (2019)","DOI":"10.1609\/aaai.v33i01.33016818"},{"issue":"1","key":"39_CR18","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1002\/cae.22059","volume":"27","author":"R Oramas Bustillos","year":"2019","unstructured":"Oramas Bustillos, R., Zatarain Cabada, R., Barr\u00f3n Estrada, M.L., Hern\u00e1ndez P\u00e9rez, Y.: Opinion mining and emotion recognition in an intelligent learning environment. Comput. Appl. Eng. Educ. 27(1), 90\u2013101 (2019)","journal-title":"Comput. Appl. Eng. Educ."},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., Morency, L.P.: Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (volume 1: Long papers), pp. 873\u2013883 (2017)","DOI":"10.18653\/v1\/P17-1081"},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: MELD: a multimodal multi-party dataset for emotion recognition in conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 527\u2013536 (2019)","DOI":"10.18653\/v1\/P19-1050"},{"issue":"10","key":"39_CR21","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"XP Qiu","year":"2020","unstructured":"Qiu, X.P., Sun, T.X., Xu, Y.G., Shao, Y.F., Dai, N., Huang, X.J.: Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63(10), 1872\u20131897 (2020). https:\/\/doi.org\/10.1007\/s11431-020-1647-3","journal-title":"Sci. China Technol. Sci."},{"key":"39_CR22","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1109\/LSP.2021.3078698","volume":"28","author":"M Ren","year":"2021","unstructured":"Ren, M., Huang, X., Shi, X., Nie, W.: Interactive multimodal attention network for emotion recognition in conversation. IEEE Signal Process. Lett. 28, 1046\u20131050 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Shaheen, S., El-Hajj, W., Hajj, H., Elbassuoni, S.: Emotion recognition from text based on automatically generated rules. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 383\u2013392. IEEE (2014)","DOI":"10.1109\/ICDMW.2014.80"},{"key":"39_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"39_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-030-16148-4_17","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Wan, Z., Wan, X.: BAB-QA: a new neural model for emotion detection in multi-party dialogue. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11439, pp. 210\u2013221. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16148-4_17"},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, D., Chen, X., Xu, S., Xu, B.: Knowledge aware emotion recognition in textual conversations via multi-task incremental transformer. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4429\u20134440 (2020)","DOI":"10.18653\/v1\/2020.coling-main.392"},{"key":"39_CR27","unstructured":"Zhang, R., Wang, Z., Huang, Z., Li, L., Zheng, M.: Predicting emotion reactions for human-computer conversation: a variational approach. IEEE Trans. Hum.-Mach. Syst. 62(8), 2937\u20132987 (2021)"},{"key":"39_CR28","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.inffus.2020.04.003","volume":"62","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., et al.: A quantum-like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis. Inf. Fusion 62, 14\u201331 (2020)","journal-title":"Inf. Fusion"},{"key":"39_CR29","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.neunet.2020.10.001","volume":"133","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis. Neural Netw. 133, 40\u201356 (2021)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92238-2_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:55:17Z","timestamp":1710356117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92238-2_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922375","9783030922382"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92238-2_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}