{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:35:02Z","timestamp":1773189302553,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Sci- ence Foundation of China","doi-asserted-by":"crossref","award":["61802444"],"award-info":[{"award-number":["61802444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004832","name":"Changsha University of Science Technology","doi-asserted-by":"publisher","award":["kq2202294"],"award-info":[{"award-number":["kq2202294"]}],"id":[{"id":"10.13039\/501100004832","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation of Education Bureau of Hunan Province of China","award":["20B625"],"award-info":[{"award-number":["20B625"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province of China","award":["18B196"],"award-info":[{"award-number":["18B196"]}]},{"name":"Research on Local Community Structure Detection Algorithms in Complex Networks","award":["2020YJ009"],"award-info":[{"award-number":["2020YJ009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04556-x","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T11:07:28Z","timestamp":1680260848000},"page":"20174-20190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A multi-semantic passing framework for semi-supervised long text classification"],"prefix":"10.1007","volume":"53","author":[{"given":"Wei","family":"Ai","sequence":"first","affiliation":[]},{"given":"Ze","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hongen","family":"Shao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9787-2002","authenticated-orcid":false,"given":"Tao","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"4556_CR1","doi-asserted-by":"crossref","unstructured":"Chen J, Gong X, Qiu Y, et al. (2021) Multi-label classification of long text based on key-sentences extraction. In: International conference on database systems for advanced applications, pp 3\u201319","DOI":"10.1007\/978-3-030-73197-7_1"},{"key":"4556_CR2","doi-asserted-by":"crossref","unstructured":"Conneau A, Schwenk H, Barrault L et al (2016) Very deep convolutional networks for text classification. arXiv:160601781","DOI":"10.18653\/v1\/E17-1104"},{"key":"4556_CR3","unstructured":"Devlin J, Chang M W, Lee K, et al. (2018) 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, pp 4171\u20134186"},{"key":"4556_CR4","doi-asserted-by":"crossref","unstructured":"Du J, Huang Y, Moilanen K (2020) Pointing to select: a fast pointer-lstm for long text classification. In: Proceedings of the 28th international conference on computational linguistics, pp 6184\u20136193","DOI":"10.18653\/v1\/2020.coling-main.544"},{"key":"4556_CR5","doi-asserted-by":"crossref","unstructured":"Du J, Huang Y, Moilanen K (2021a) Knowledge-aware leap-lstm: integrating prior knowledge into leap-lstm towards faster long text classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 12768\u201312775","DOI":"10.1609\/aaai.v35i14.17511"},{"issue":"3","key":"4556_CR6","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TCYB.2020.2969705","volume":"51","author":"J Du","year":"2021","unstructured":"Du J, Vong C M, Chen C L P (2021b) Novel efficient rnn and lstm-like architectures: recurrent and gated broad learning systems and their applications for text classification. IEEE Trans Cybern 51 (3):1586\u20131597","journal-title":"IEEE Trans Cybern"},{"key":"4556_CR7","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30"},{"key":"4556_CR8","doi-asserted-by":"crossref","unstructured":"Johnson R, Zhang T (2017) Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th annual meeting of the association for computational linguistics, pp 562\u2013570","DOI":"10.18653\/v1\/P17-1052"},{"key":"4556_CR9","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"4556_CR10","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:160902907"},{"key":"4556_CR11","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems"},{"key":"4556_CR12","doi-asserted-by":"crossref","unstructured":"Lan Z, Chen M, Goodman S, et al. (2020) Albert: a lite bert for self-supervised learning of language representations. In: International conference on learning representations","DOI":"10.1109\/SLT48900.2021.9383575"},{"key":"4556_CR13","doi-asserted-by":"crossref","unstructured":"Linmei H, Yang T, Shi C, et al. (2019) Heterogeneous graph attention networks for semi-supervised short text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 4821\u2013 4830","DOI":"10.18653\/v1\/D19-1488"},{"key":"4556_CR14","unstructured":"Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 2873\u20132879"},{"key":"4556_CR15","unstructured":"Mikolov T, Chen K, Corrado G, et al. (2013) Efficient estimation of word representations in vector space. Proceedings of workshop at ICLR"},{"issue":"3","key":"4556_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2021","unstructured":"Minaee S, Kalchbrenner N, Cambria E, et al. (2021) Deep learning\u2013based text classification: a comprehensive review. ACM Comput Surv 54(3):1\u201340","journal-title":"ACM Comput Surv"},{"key":"4556_CR17","doi-asserted-by":"crossref","unstructured":"Monti F, Otness K, Bronstein M M (2018) Motifnet: a motif-based graph convolutional network for directed graphs. In: 2018 IEEE data science workshop, pp 225\u2013228","DOI":"10.1109\/DSW.2018.8439897"},{"issue":"6","key":"4556_CR18","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1109\/TKDE.2019.2959991","volume":"33","author":"H Peng","year":"2021","unstructured":"Peng H, Li J, Wang S, et al. (2021) Hierarchical taxonomy-aware and attentional graph capsule rcnns for large-scale multi-label text classification. IEEE Trans Knowl Data Eng 33(6):2505\u20132519","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4556_CR19","unstructured":"Radford A, Narasimhan K (2018) Improving language understanding by generative pre-training"},{"key":"4556_CR20","doi-asserted-by":"crossref","unstructured":"Ragesh R, Sellamanickam S, Iyer A, et al. (2021) Hetegcn: heterogeneous graph convolutional networks for text classification. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 860\u2013868","DOI":"10.1145\/3437963.3441746"},{"issue":"3","key":"4556_CR21","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TNNLS.2020.3036192","volume":"33","author":"Z Tan","year":"2022","unstructured":"Tan Z, Chen J, Kang Q, et al. (2022) Dynamic embedding projection-gated convolutional neural networks for text classification. IEEE Trans Neural Netw Learn Syst 33(3):973\u2013982","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4556_CR22","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Mei Q (2015) Pte: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1165\u20131174","DOI":"10.1145\/2783258.2783307"},{"key":"4556_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. Advances in Neural Information Processing Systems 30"},{"key":"4556_CR24","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et al. (2018) Graph attention networks. In: International conference on learning representations"},{"key":"4556_CR25","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, et al. (2019) Heterogeneous graph attention network. In: The world wide web conference, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"4556_CR26","doi-asserted-by":"crossref","unstructured":"Weijie D, Yunyi L, Jing Z, et al. (2021) Long text classification based on bert. In: 2021 IEEE 5th information technology, networking, electronic and automation control conference (ITNEC), pp 1147\u20131151","DOI":"10.1109\/ITNEC52019.2021.9587007"},{"issue":"3","key":"4556_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450352","volume":"39","author":"T Yang","year":"2021","unstructured":"Yang T, Hu L, Shi C, et al. (2021) Hgat: heterogeneous graph attention networks for semi-supervised short text classification. ACM Trans Inf Syst 39(3):1\u201329","journal-title":"ACM Trans Inf Syst"},{"key":"4556_CR28","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, et al. (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"4556_CR29","unstructured":"Yang Z, Dai Z, Yang Y, et al. (2019) Xlnet: generalized autoregressive pretraining for language understanding. Advances in Neura l Information Processing Systems 32"},{"key":"4556_CR30","doi-asserted-by":"crossref","unstructured":"Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 7370\u20137377","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"4556_CR31","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv:14092329"},{"key":"4556_CR32","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, et al. (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"4556_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yu X, Cui Z, et al. (2020) Every document owns its structure: inductive text classification via graph neural networks. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 334\u2013339","DOI":"10.18653\/v1\/2020.acl-main.31"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04556-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04556-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04556-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:42:42Z","timestamp":1729168962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04556-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":33,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4556"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04556-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,31]]},"assertion":[{"value":"1 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2023","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}