{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T04:06:03Z","timestamp":1744085163209,"version":"3.40.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientic Research Funds project of Science and Technology Department of Sichuan Province","award":["2019YFG0508, 2019GFW131, 2024JY**, 24NSFJQ0015"],"award-info":[{"award-number":["2019YFG0508, 2019GFW131, 2024JY**, 24NSFJQ0015"]}]},{"DOI":"10.13039\/501100018525","name":"Sichuan Key R$&$D project","doi-asserted-by":"crossref","award":["2023YFG0354"],"award-info":[{"award-number":["2023YFG0354"]}],"id":[{"id":"10.13039\/501100018525","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61902324"],"award-info":[{"award-number":["61902324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Funds Project of Chengdu Science and Technology Bureau","award":["2017-RK00-00026-ZF, 2022-YF04- 00065-JH, 2023-JB00-00020-GX"],"award-info":[{"award-number":["2017-RK00-00026-ZF, 2022-YF04- 00065-JH, 2023-JB00-00020-GX"]}]},{"name":"Xihua University Education and teaching reform project","award":["xjjg2021049, xjjg2021115"],"award-info":[{"award-number":["xjjg2021049, xjjg2021115"]}]},{"name":"Science and Technology Planning Project of Guizhou Province","award":["QianKeHeJiChu-ZK[2021]YiBan319"],"award-info":[{"award-number":["QianKeHeJiChu-ZK[2021]YiBan319"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10115-024-02313-1","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T14:57:59Z","timestamp":1736175479000},"page":"3317-3342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perceptive dual-graph semantic integration network for aspect sentiment triplet extraction"],"prefix":"10.1007","volume":"67","author":[{"given":"Ya","family":"Wen","sequence":"first","affiliation":[]},{"given":"Liansong","family":"Zong","sequence":"additional","affiliation":[]},{"given":"Mingwei","family":"Tang","sequence":"additional","affiliation":[]},{"given":"LinPing","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"2313_CR1","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De\u00a0Clercq O (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on semantic evaluation (SemEval-2016), pp 19\u201330 . Association for Computational Linguistics","DOI":"10.18653\/v1\/S16-1002"},{"key":"2313_CR2","doi-asserted-by":"crossref","unstructured":"Peng H, Xu L, Bing L, Huang F, Lu W, Si L (2020) Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 8600\u20138607","DOI":"10.1609\/aaai.v34i05.6383"},{"key":"2313_CR3","doi-asserted-by":"crossref","unstructured":"Li K, Chen C, Quan X, Ling Q, Song Y (2020) Conditional augmentation for aspect term extraction via masked sequence-to-sequence generation. arXiv preprint arXiv:2004.14769","DOI":"10.18653\/v1\/2020.acl-main.631"},{"key":"2313_CR4","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893","DOI":"10.24963\/ijcai.2017\/568"},{"key":"2313_CR5","doi-asserted-by":"crossref","unstructured":"Wu Z, Zhao F, Dai X-Y, Huang S, Chen J (2020) Latent opinions transfer network for target-oriented opinion words extraction. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 9298\u20139305","DOI":"10.1609\/aaai.v34i05.6469"},{"key":"2313_CR6","doi-asserted-by":"crossref","unstructured":"Xu L, Chia YK, Bing L (2021) Learning span-level interactions for aspect sentiment triplet extraction. arXiv preprint arXiv:2107.12214","DOI":"10.18653\/v1\/2021.acl-long.367"},{"key":"2313_CR7","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang Y, Li Y, Liang B, Chen S, Dang Y, Yang M, Xu R (2022) Boundary-driven table-filling for aspect sentiment triplet extraction. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 6485\u20136498","DOI":"10.18653\/v1\/2022.emnlp-main.435"},{"key":"2313_CR8","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.neucom.2022.07.067","volume":"507","author":"L Shi","year":"2022","unstructured":"Shi L, Han D, Han J, Qiao B, Wu G (2022) Dependency graph enhanced interactive attention network for aspect sentiment triplet extraction. Neurocomputing 507:315\u2013324","journal-title":"Neurocomputing"},{"key":"2313_CR9","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neucom.2020.08.013","volume":"420","author":"K Shuang","year":"2021","unstructured":"Shuang K, Gu M, Li R, Loo J, Su S (2021) Interactive pos-aware network for aspect-level sentiment classification. Neurocomputing 420:181\u2013196","journal-title":"Neurocomputing"},{"key":"2313_CR10","doi-asserted-by":"crossref","unstructured":"Yuan L, Wang J, Yu L-C, Zhang X (2023) Encoding syntactic information into transformers for aspect-based sentiment triplet extraction. IEEE Trans Affect Comput","DOI":"10.1109\/TAFFC.2023.3291730"},{"key":"2313_CR11","doi-asserted-by":"publisher","first-page":"108366","DOI":"10.1016\/j.knosys.2022.108366","volume":"242","author":"Y Li","year":"2022","unstructured":"Li Y, Lin Y, Lin Y, Chang L, Zhang H (2022) A span-sharing joint extraction framework for harvesting aspect sentiment triplets. Knowl-Based Syst 242:108366","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"2313_CR12","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1007\/s10115-022-01675-8","volume":"64","author":"Y Li","year":"2022","unstructured":"Li Y, Wang C, Lin Y, Lin Y, Chang L (2022) Span-based relational graph transformer network for aspect-opinion pair extraction. Knowl Inf Syst 64(5):1305\u20131322","journal-title":"Knowl Inf Syst"},{"key":"2313_CR13","doi-asserted-by":"crossref","unstructured":"Li P, Li P, Zhang K (2023) Dual-channel span for aspect sentiment triplet extraction. In: Proceedings of the 2023 conference on empirical methods in natural language processing, pp 248\u2013261","DOI":"10.18653\/v1\/2023.emnlp-main.17"},{"key":"2313_CR14","unstructured":"Zhao X, Zhou Y, Xu X (2024) Dual encoder: exploiting the potential of syntactic and semantic for aspect sentiment triplet extraction. arXiv preprint arXiv:2402.15370"},{"key":"2313_CR15","doi-asserted-by":"crossref","unstructured":"Li Y, Zeng X, Zeng Y, Lin Y (2024) Enhanced packed marker with entity information for aspect sentiment triplet extraction. In: Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp 619\u2013629","DOI":"10.1145\/3626772.3657734"},{"key":"2313_CR16","doi-asserted-by":"crossref","unstructured":"Chen Z, Qian T (2020) Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3685\u20133694","DOI":"10.18653\/v1\/2020.acl-main.340"},{"issue":"14","key":"2313_CR17","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.3390\/math11143165","volume":"11","author":"Y Li","year":"2023","unstructured":"Li Y, Wang F, Zhong S-h (2023) A more fine-grained aspect-sentiment-opinion triplet extraction task. Mathematics 11(14):3165","journal-title":"Mathematics"},{"key":"2313_CR18","doi-asserted-by":"crossref","unstructured":"Mukherjee R, Nayak T, Butala Y, Bhattacharya S, Goyal P (2021) Paste: a tagging-free decoding framework using pointer networks for aspect sentiment triplet extraction. arXiv preprint arXiv:2110.04794","DOI":"10.18653\/v1\/2021.emnlp-main.731"},{"key":"2313_CR19","doi-asserted-by":"crossref","unstructured":"Chen Y, Chen K, Sun X, Zhang Z (2022) A span-level bidirectional network for aspect sentiment triplet extraction. arXiv preprint arXiv:2204.12674","DOI":"10.18653\/v1\/2022.emnlp-main.289"},{"key":"2313_CR20","doi-asserted-by":"crossref","unstructured":"Zhong Q, Jiao X, Que Y, Chen J (2023) Span-based attention decoder framework for aspect sentiment triplet extraction. In: 2023 IEEE 3rd international conference on computer systems (ICCS), pp 188\u2013193 . IEEE","DOI":"10.1109\/ICCS59700.2023.10335545"},{"key":"2313_CR21","doi-asserted-by":"crossref","unstructured":"Wu Z, Ying C, Zhao F, Fan Z, Dai X, Xia R (2020) Grid tagging scheme for aspect-oriented fine-grained opinion extraction. arXiv preprint arXiv:2010.04640","DOI":"10.18653\/v1\/2020.findings-emnlp.234"},{"key":"2313_CR22","doi-asserted-by":"crossref","unstructured":"Xu L, Li H, Lu W, Bing L (2020) Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609","DOI":"10.18653\/v1\/2020.emnlp-main.183"},{"key":"2313_CR23","doi-asserted-by":"publisher","first-page":"108366","DOI":"10.1016\/j.knosys.2022.108366","volume":"242","author":"Y Li","year":"2022","unstructured":"Li Y, Lin Y, Lin Y, Chang L, Zhang H (2022) A span-sharing joint extraction framework for harvesting aspect sentiment triplets. Knowl-Based Syst 242:108366","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"2313_CR24","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1109\/TAFFC.2022.3202831","volume":"14","author":"K He","year":"2022","unstructured":"He K, Mao R, Gong T, Li C, Cambria E (2022) Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Trans Affect Comput 14(3):1731\u20131742","journal-title":"IEEE Trans Affect Comput"},{"key":"2313_CR25","doi-asserted-by":"crossref","unstructured":"Xu J, Yang S, Xiao L, Fu Z, Wu X, Ma T, He L (2022) Graph convolution over the semantic-syntactic hybrid graph enhanced by affective knowledge for aspect-level sentiment classification. In: 2022 international joint conference on neural networks (IJCNN), pp 1\u20138 . IEEE","DOI":"10.1109\/IJCNN55064.2022.9892027"},{"key":"2313_CR26","doi-asserted-by":"publisher","first-page":"110662","DOI":"10.1016\/j.knosys.2023.110662","volume":"275","author":"S Zhang","year":"2023","unstructured":"Zhang S, Gong H, She L (2023) An aspect sentiment classification model for graph attention networks incorporating syntactic, semantic, and knowledge. Knowl-Based Syst 275:110662","journal-title":"Knowl-Based Syst"},{"key":"2313_CR27","doi-asserted-by":"publisher","first-page":"102304","DOI":"10.1016\/j.inffus.2024.102304","volume":"106","author":"L Xiao","year":"2024","unstructured":"Xiao L, Wu X, Xu J, Li W, Jin C, He L (2024) Atlantis: aesthetic-oriented multiple granularities fusion network for joint multimodal aspect-based sentiment analysis. Inf Fusion 106:102304","journal-title":"Inf Fusion"},{"key":"2313_CR28","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.10.091","volume":"471","author":"L Xiao","year":"2022","unstructured":"Xiao L, Xue Y, Wang H, Hu X, Gu D, Zhu Y (2022) Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks. Neurocomputing 471:48\u201359","journal-title":"Neurocomputing"},{"key":"2313_CR29","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"2313_CR30","doi-asserted-by":"crossref","unstructured":"Chakraborty M, Kulkarni A, Li Q (2022) Open-domain aspect-opinion co-mining with double-layer span extraction. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 66\u201375","DOI":"10.1145\/3534678.3539386"},{"key":"2313_CR31","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"2313_CR32","doi-asserted-by":"crossref","unstructured":"Li J, Wen Y, He L (2023) Scconv: spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6153\u20136162","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"2313_CR33","unstructured":"Hendrycks D, Gimpel K (2016) Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415"},{"key":"2313_CR34","doi-asserted-by":"crossref","unstructured":"Dai H, Song Y (2019) Neural aspect and opinion term extraction with mined rules as weak supervision. arXiv preprint arXiv:1907.03750","DOI":"10.18653\/v1\/P19-1520"},{"key":"2313_CR35","doi-asserted-by":"crossref","unstructured":"Chen S, Wang Y, Liu J, Wang Y (2021) Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 12666\u201312674","DOI":"10.1609\/aaai.v35i14.17500"},{"key":"2313_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2022.04.022","volume":"492","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zhang Z, Zhou G, Sun X, Chen K (2022) Span-based dual-decoder framework for aspect sentiment triplet extraction. Neurocomputing 492:211\u2013221","journal-title":"Neurocomputing"},{"key":"2313_CR37","doi-asserted-by":"crossref","unstructured":"Chen H, Zhai Z, Feng F, Li R, Wang X (2022) Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 2974\u20132985","DOI":"10.18653\/v1\/2022.acl-long.212"},{"key":"2313_CR38","doi-asserted-by":"publisher","first-page":"1193011","DOI":"10.3389\/fnbot.2023.1193011","volume":"17","author":"Y Li","year":"2023","unstructured":"Li Y, He Q, Zhang D (2023) Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction. Front Neurorobot 17:1193011","journal-title":"Front Neurorobot"},{"key":"2313_CR39","doi-asserted-by":"crossref","unstructured":"Yu G, Liu L, Jiang H, Shi S, Ao X (2023) Making better use of training corpus: retrieval-based aspect sentiment triplet extraction via label interpolation. In: Findings of the association for computational linguistics: ACL 2023, pp 4914\u20134927","DOI":"10.18653\/v1\/2023.findings-acl.303"},{"issue":"5","key":"2313_CR40","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.3390\/app14052221","volume":"14","author":"J Peng","year":"2024","unstructured":"Peng J, Su B (2024) Aspect sentiment triplet extraction based on deep relationship enhancement networks. Appl Sci 14(5):2221","journal-title":"Appl Sci"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02313-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-024-02313-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02313-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T05:27:08Z","timestamp":1744003628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-024-02313-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2313"],"URL":"https:\/\/doi.org\/10.1007\/s10115-024-02313-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"27 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}