{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:45:02Z","timestamp":1764225902889,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s13042-023-02010-5","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T05:01:36Z","timestamp":1699506096000},"page":"1995-2011","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A multi-perspective global\u2013local interaction framework for identifying dialogue acts and sentiments of dialogue utterances jointly"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1618-7765","authenticated-orcid":false,"given":"Qichen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingmei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"issue":"2","key":"2010_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1145\/3166054.3166058","volume":"19","author":"H Chen","year":"2017","unstructured":"Chen H, Liu X, Yin D, Tang J (2017) A survey on dialogue systems: Recent advances and new frontiers. ACM SIGKDD Explorations Newsl 19(2):25\u201335","journal-title":"ACM SIGKDD Explorations Newsl"},{"issue":"4","key":"2010_CR2","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1007\/s10462-022-10248-8","volume":"56","author":"J Ni","year":"2023","unstructured":"Ni J, Young T, Pandelea V, Xue F, Cambria E (2023) Recent advances in deep learning based dialogue systems: A systematic survey. Artif Intell Rev 56(4):3055\u20133155","journal-title":"Artif Intell Rev"},{"issue":"1","key":"2010_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-02145-9","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu B (2012) Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1):1\u2013167","journal-title":"Synthesis lectures on human language technologies"},{"key":"2010_CR4","unstructured":"Fung P, Dey A, Siddique FB, Lin R, Yang Y, Bertero D, Wan Y, Chan RHY, Wu C-S (2016) Zara: A virtual interactive dialogue system incorporating emotion, sentiment and personality recognition. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pp. 278\u2013281"},{"key":"2010_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2017.11.009","volume":"101","author":"M Kim","year":"2018","unstructured":"Kim M, Kim H (2018) Integrated neural network model for identifying speech acts, predicators, and sentiments of dialogue utterances. Pattern Recogn Lett 101:1\u20135","journal-title":"Pattern Recogn Lett"},{"key":"2010_CR6","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.inffus.2020.06.011","volume":"64","author":"Y Ma","year":"2020","unstructured":"Ma Y, Nguyen KL, Xing FZ, Cambria E (2020) A survey on empathetic dialogue systems. Information Fusion 64:50\u201370","journal-title":"Information Fusion"},{"key":"2010_CR7","unstructured":"Cerisara C, Jafaritazehjani S, Oluokun A, Le HT (2018) Multi-task dialog act and sentiment recognition on mastodon. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 745\u2013754"},{"key":"2010_CR8","doi-asserted-by":"crossref","unstructured":"Qin L, Che W, Li Y, Ni M, Liu T (2020) Dcr-net: A deep co-interactive relation network for joint dialog act recognition and sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8665\u20138672","DOI":"10.1609\/aaai.v34i05.6391"},{"key":"2010_CR9","doi-asserted-by":"crossref","unstructured":"Li J, Fei H, Ji D (2020) 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","DOI":"10.18653\/v1\/2020.coling-main.53"},{"key":"2010_CR10","doi-asserted-by":"crossref","unstructured":"Qin L, Li Z, Che W, Ni M, Liu T (2021) Co-gat: A co-interactive graph attention network for joint dialog act recognition and sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 13709\u201313717","DOI":"10.1609\/aaai.v35i15.17616"},{"key":"2010_CR11","doi-asserted-by":"crossref","unstructured":"Xing B, Tsang I (2022) Darer: Dual-task temporal relational recurrent reasoning network for joint dialog sentiment classification and act recognition. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 3611\u20133621","DOI":"10.18653\/v1\/2022.findings-acl.286"},{"key":"2010_CR12","doi-asserted-by":"crossref","unstructured":"Sordoni A, Galley M, Auli M, Brockett C, Ji Y, Mitchell M, Nie J-Y, Gao J, Dolan WB (2015) A neural network approach to context-sensitive generation of conversational responses. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 196\u2013205","DOI":"10.3115\/v1\/N15-1020"},{"key":"2010_CR13","unstructured":"Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) 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"},{"key":"2010_CR14","doi-asserted-by":"crossref","unstructured":"Chen Z, Yang R, Zhao Z, Cai D, He X (2018) Dialogue act recognition via crf-attentive structured network. In: The 41st International Acm Sigir Conference on Research & Development in Information Retrieval, pp. 225\u2013234","DOI":"10.1145\/3209978.3209997"},{"key":"2010_CR15","doi-asserted-by":"crossref","unstructured":"Kumar H, Agarwal A, Dasgupta R, Joshi S (2018) Dialogue act sequence labeling using hierarchical encoder with crf. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 3440\u20133447","DOI":"10.1609\/aaai.v32i1.11701"},{"key":"2010_CR16","unstructured":"Raheja V, Tetreault J (2019) Dialogue act classification with context-aware self-attention. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 3727\u20133733"},{"key":"2010_CR17","doi-asserted-by":"crossref","unstructured":"Li R, Lin C, Collinson M, Li X, Chen G (2019) A dual-attention hierarchical recurrent neural network for dialogue act classification. In: 23rd Conference on Computational Natural Language Learning, CoNLL 2019, pp. 383\u2013392. Association for Computational Linguistics","DOI":"10.18653\/v1\/K19-1036"},{"key":"2010_CR18","doi-asserted-by":"crossref","unstructured":"Colombo P, Chapuis E, Manica M, Vignon E, Varni G, Clavel C (2020) Guiding attention in sequence-to-sequence models for dialogue act prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 7594\u20137601","DOI":"10.1609\/aaai.v34i05.6259"},{"key":"2010_CR19","doi-asserted-by":"crossref","unstructured":"He Z, Tavabi L, Lerman K, Soleymani M (2021) Speaker turn modeling for dialogue act classification. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2150\u20132157","DOI":"10.18653\/v1\/2021.findings-emnlp.185"},{"key":"2010_CR20","doi-asserted-by":"crossref","unstructured":"Wu T-W, Su R, Juang B-H (2021) A context-aware hierarchical bert fusion network for multi-turn dialog act detection. arXiv preprint arXiv:2109.01267","DOI":"10.21437\/Interspeech.2021-95"},{"key":"2010_CR21","unstructured":"Pengfei G, Yinglong M (2022) A universality-individuality integration model for dialog act classification. arXiv preprint arXiv:2204.06185"},{"key":"2010_CR22","doi-asserted-by":"crossref","unstructured":"Gella S, Padmakumar A, Lange PL, Hakkani-Tur D (2022) Dialog acts for task driven embodied agents. In: Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 111\u2013123","DOI":"10.18653\/v1\/2022.sigdial-1.13"},{"key":"2010_CR23","doi-asserted-by":"crossref","unstructured":"Chang F-J, Muniyappa T, Sathyendra KM, Wei K, Strimel GP, McGowan R (2023) Dialog act guided contextual adapter for personalized speech recognition. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10094707"},{"issue":"4","key":"2010_CR24","first-page":"1253","volume":"8","author":"L Zhang","year":"2018","unstructured":"Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4):1253","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"2010_CR25","doi-asserted-by":"crossref","unstructured":"Qiu M, Huang X, Chen C, Ji F, Qu C, Wei W, Huang J, Zhang Y (2021) Reinforced history backtracking for conversational question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 13718\u201313726","DOI":"10.1609\/aaai.v35i15.17617"},{"key":"2010_CR26","doi-asserted-by":"crossref","unstructured":"Musto C, de Gemmis M, Semeraro G, Lops P (2017) A multi-criteria recommender system exploiting aspect-based sentiment analysis of users\u2019 reviews. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 321\u2013325","DOI":"10.1145\/3109859.3109905"},{"issue":"2","key":"2010_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3432049","volume":"39","author":"P Liu","year":"2021","unstructured":"Liu P, Zhang L, Gulla JA (2021) Multilingual review-aware deep recommender system via aspect-based sentiment analysis. ACM Transactions on Information Systems (TOIS) 39(2):1\u201333","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"2010_CR28","doi-asserted-by":"crossref","unstructured":"Ghosal D, Majumder N, Poria S, Chhaya N, Gelbukh A (2019) 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","DOI":"10.18653\/v1\/D19-1015"},{"key":"2010_CR29","doi-asserted-by":"crossref","unstructured":"Majumder N, Poria S, Hazarika D, Mihalcea R, Gelbukh A, Cambria E (2019) Dialoguernn: An attentive rnn for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6818\u20136825","DOI":"10.1609\/aaai.v33i01.33016818"},{"key":"2010_CR30","doi-asserted-by":"crossref","unstructured":"Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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. 4568\u20134578","DOI":"10.18653\/v1\/D19-1464"},{"key":"2010_CR31","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1109\/TASLP.2020.3042009","volume":"29","author":"X Bai","year":"2020","unstructured":"Bai X, Liu P, Zhang Y (2020) Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network. IEEE\/ACM Transactions on Audio, Speech, and Language Processing 29:503\u2013514","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"2010_CR32","doi-asserted-by":"crossref","unstructured":"Shen W, Wu S, Yang Y, Quan X (2021) Directed acyclic graph network for conversational emotion recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1551\u20131560","DOI":"10.18653\/v1\/2021.acl-long.123"},{"key":"2010_CR33","unstructured":"Augustine E, Jandaghi P, Albalak A, Pryor C, Dickens C, Wang W, Getoor L (2022) Emotion recognition in conversation using probabilistic soft logic. arXiv preprint arXiv:2207.07238"},{"key":"2010_CR34","doi-asserted-by":"crossref","unstructured":"Ghazarian S, Hedayatnia B, Papangelis A, Liu Y, Hakkani-Tur D (2022) What is wrong with you?: Leveraging user sentiment for automatic dialog evaluation. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 4194\u20134204","DOI":"10.18653\/v1\/2022.findings-acl.331"},{"key":"2010_CR35","doi-asserted-by":"crossref","unstructured":"Cheng Z, Zhou J, Wu W, Chen Q, He L (2023) Tell model where to attend: Improving interpretability of aspect-based sentiment classification via small explanation annotations. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10096952"},{"key":"2010_CR36","doi-asserted-by":"crossref","unstructured":"Kumar S, Mondal I, Akhtar MS, Chakraborty T (2023) Explaining (sarcastic) utterances to enhance affect understanding in multimodal dialogues. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 12986\u201312994","DOI":"10.1609\/aaai.v37i11.26526"},{"key":"2010_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"2010_CR38","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"2010_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2010_CR40","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"2010_CR41","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186"},{"key":"2010_CR42","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"2010_CR43","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 5753\u20135763"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02010-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02010-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02010-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T14:23:33Z","timestamp":1712931813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02010-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,9]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["2010"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02010-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,11,9]]},"assertion":[{"value":"5 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}