{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T07:14:32Z","timestamp":1784272472199,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"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 Science Foundation of China","doi-asserted-by":"publisher","award":["62062066"],"award-info":[{"award-number":["62062066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61962061"],"award-info":[{"award-number":["61962061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yunnan Provincial Foundation for Leaders of Disciplines in Science and Technology","award":["202005AC160005"],"award-info":[{"award-number":["202005AC160005"]}]},{"name":"the Key Program of Basic Research of Yunnan Province","award":["202201AS070015"],"award-info":[{"award-number":["202201AS070015"]}]},{"name":"the Key Program of Basic Research of Yunnan Province","award":["202101AS070056"],"award-info":[{"award-number":["202101AS070056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05485-z","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T12:01:20Z","timestamp":1715256080000},"page":"6269-6284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["TAE: Topic-aware encoder for large-scale multi-label text classification"],"prefix":"10.1007","volume":"54","author":[{"given":"Shaowei","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3696-9281","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiji","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"issue":"6","key":"5485_CR1","doi-asserted-by":"publisher","first-page":"3330","DOI":"10.1109\/TSC.2021.3098756","volume":"15","author":"H Wu","year":"2022","unstructured":"Wu H, Duan Y, Yue K et al (2022) Mashup-oriented Web API recommendation via multi-model fusion and multi-task learning. IEEE Trans Serv Comput 15(6):3330\u20133343","journal-title":"IEEE Trans Serv Comput"},{"issue":"11","key":"5485_CR2","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","volume":"44","author":"W Liu","year":"2022","unstructured":"Liu W, Wang H, Shen X et al (2022) The emerging trends of multi-label learning. IEEE Trans Pattern Anal Mach Intell 44(11):7955\u20137974","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5485_CR3","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"2","key":"5485_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/3373464.3373474","volume":"21","author":"S Burkhardt","year":"2019","unstructured":"Burkhardt S, Kramer S (2019) A survey of multi-label topic models. SIGKDD Explor 21(2):61\u201379","journal-title":"SIGKDD Explor"},{"issue":"1","key":"5485_CR5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TNNLS.2021.3094987","volume":"34","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Liao L, Gao Y et al (2023) Topicbert: A topic-enhanced neural language model fine-tuned for sentiment classification. IEEE Trans Neural Networks Learn Syst 34(1):380\u2013393","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"5485_CR6","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 (EMNLP), pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"#cr-split#-5485_CR7.1","doi-asserted-by":"crossref","unstructured":"Zhao Y, Shen Y, Yao J (2019) Recurrent neural network for text classification with hierarchical multiscale dense connections. In: Kraus S","DOI":"10.24963\/ijcai.2019\/757"},{"key":"#cr-split#-5485_CR7.2","unstructured":"(ed) Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10-16, 2019. ijcai.org, pp 5450-5456"},{"key":"5485_CR8","unstructured":"Devlin J, Chang MW, Lee K et\u00a0al (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, pp 4171\u20134186"},{"key":"5485_CR9","doi-asserted-by":"crossref","unstructured":"Liu J, Chang WC, Wu Y, et\u00a0al (2017) Deep learning for extreme multi-label text classification. In: Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval (SIGIR), ACM, pp 115\u2013124","DOI":"10.1145\/3077136.3080834"},{"key":"5485_CR10","unstructured":"You R, Zhang Z, Wang Z, et\u00a0al (2019) AttentionXML: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification. In: Advances in neural information processing systems 32: annual conference on neural information processing systems 2019, pp 5812\u20135822"},{"issue":"12","key":"5485_CR11","doi-asserted-by":"publisher","first-page":"2322","DOI":"10.1109\/TKDE.2019.2922179","volume":"32","author":"P Zhang","year":"2020","unstructured":"Zhang P, Wang S, Li D et al (2020) Combine topic modeling with semantic embedding: embedding enhanced topic model. IEEE Trans Knowl Data Eng 32(12):2322\u20132335","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"15","key":"5485_CR12","doi-asserted-by":"publisher","first-page":"17829","DOI":"10.1007\/s10489-022-03388-5","volume":"52","author":"M Pita","year":"2022","unstructured":"Pita M, Nunes M, Pappa GL (2022) Probabilistic topic modeling for short text based on word embedding networks. Appl Intell 52(15):17829\u201317844","journal-title":"Appl Intell"},{"issue":"2","key":"5485_CR13","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1007\/s10489-020-01838-6","volume":"51","author":"Z Chen","year":"2021","unstructured":"Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966\u2013979","journal-title":"Appl Intell"},{"key":"5485_CR14","doi-asserted-by":"crossref","unstructured":"Qiu S, Sekhar N, Singhal P (2023) Topic and style-aware transformer for multimodal emotion recognition. In: Rogers A, Boyd-Graber JL, Okazaki N (eds) Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023. Association for Computational Linguistics, pp 2074\u20132082","DOI":"10.18653\/v1\/2023.findings-acl.130"},{"issue":"3","key":"5485_CR15","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 Networks Learn Syst 33(3):973\u2013982","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"10","key":"5485_CR16","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1109\/TNNLS.2021.3069647","volume":"33","author":"H Wu","year":"2022","unstructured":"Wu H, Qin S, Nie R et al (2022) Effective collaborative representation learning for multilabel text categorization. IEEE Trans Neural Networks Learn Syst 33(10):5200\u20135214","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"10","key":"5485_CR17","doi-asserted-by":"publisher","first-page":"4748","DOI":"10.1109\/TNNLS.2020.3019804","volume":"32","author":"Y Hou","year":"2021","unstructured":"Hou Y, Wan S, Bao F et al (2021) Gated value network for multilabel classification. IEEE Trans Neural Networks Learn Syst 32(10):4748\u20134754","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"2","key":"5485_CR18","doi-asserted-by":"publisher","first-page":"102441","DOI":"10.1016\/j.ipm.2020.102441","volume":"58","author":"R Wang","year":"2021","unstructured":"Wang R, Ridley R, Su X et al (2021) A novel reasoning mechanism for multi-label text classification. Inf Process Manag 58(2):102441","journal-title":"Inf Process Manag"},{"key":"5485_CR19","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhang Q, Yan Z, et\u00a0al (2021) Enhancing label correlation feedback in multi-label text classification via multi-task learning. In: Findings of the association for computational linguistics: ACL\/IJCNLP, pp 1190\u20131200","DOI":"10.18653\/v1\/2021.findings-acl.101"},{"issue":"11","key":"5485_CR20","doi-asserted-by":"publisher","first-page":"10992","DOI":"10.1109\/TKDE.2022.3223067","volume":"35","author":"J Chen","year":"2023","unstructured":"Chen J, Zhang R, Xu J et al (2023) A neural expectation-maximization framework for noisy multi-label text classification. IEEE Trans Knowl Data Eng 35(11):10992\u201311003","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5485_CR21","doi-asserted-by":"crossref","unstructured":"Xu P, Xiao L, Liu B, et\u00a0al (2023) Label-specific feature augmentation for long-tailed multi-label text classification. In: Thirty-Seventh AAAI conference on artificial intelligence, AAAI 2023. AAAI Press, pp 10602\u201310610","DOI":"10.1609\/aaai.v37i9.26259"},{"key":"5485_CR22","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.ins.2018.09.001","volume":"471","author":"RA Stein","year":"2019","unstructured":"Stein RA, Jaques PA, Valiati JF (2019) An analysis of hierarchical text classification using word embeddings. Inf Sci 471:216\u2013232","journal-title":"Inf Sci"},{"issue":"8","key":"5485_CR23","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R et al (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9","journal-title":"OpenAI blog"},{"key":"5485_CR24","doi-asserted-by":"publisher","first-page":"108271","DOI":"10.1016\/j.patcog.2021.108271","volume":"122","author":"L Maltoudoglou","year":"2022","unstructured":"Maltoudoglou L, Paisios A, Lenc L et al (2022) Well-calibrated confidence measures for multi-label text classification with a large number of labels. Pattern Recognit 122:108271","journal-title":"Pattern Recognit"},{"key":"5485_CR25","doi-asserted-by":"publisher","first-page":"105750","DOI":"10.1016\/j.knosys.2020.105750","volume":"196","author":"S Qin","year":"2020","unstructured":"Qin S, Wu H, Nie R et al (2020) Deep model with neighborhood-awareness for text tagging. Knowl Based Syst 196:105750","journal-title":"Knowl Based Syst"},{"key":"5485_CR26","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.ins.2019.02.021","volume":"485","author":"J Lee","year":"2019","unstructured":"Lee J, Yu I, Park J et al (2019) Memetic feature selection for multilabel text categorization using label frequency difference. Inf Sci 485:263\u2013280","journal-title":"Inf Sci"},{"key":"5485_CR27","doi-asserted-by":"crossref","unstructured":"Wang G, Li C, Wang W, et\u00a0al (2018) Joint embedding of words and labels for text classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics( ACL), pp 2321\u20132331","DOI":"10.18653\/v1\/P18-1216"},{"key":"5485_CR28","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1162\/tacl_a_00259","volume":"7","author":"N Pappas","year":"2019","unstructured":"Pappas N, Henderson J (2019) GILE: A generalized input-label embedding for text classification. Trans Assoc Comput Linguist 7:139\u2013155","journal-title":"Trans Assoc Comput Linguist"},{"key":"5485_CR29","doi-asserted-by":"crossref","unstructured":"Zhang Q, Zhang X, Yan Z, et\u00a0al (2021) Correlation-guided representation for multi-label text classification. In: Proceedings of the Thirtieth international joint conference on artificial intelligence (IJCAI), pp 3363\u20133369","DOI":"10.24963\/ijcai.2021\/463"},{"key":"5485_CR30","doi-asserted-by":"crossref","unstructured":"Jiang T, Wang D, Sun L, et\u00a0al (2021) Lightxml: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification. In: Proceedings of Thirty-Fifth AAAI conference on artificial intelligence, pp 7987\u20137994","DOI":"10.1609\/aaai.v35i9.16974"},{"issue":"7","key":"5485_CR31","first-page":"6698","volume":"35","author":"D Zong","year":"2023","unstructured":"Zong D, Sun S (2023) BGNN-XML: bilateral graph neural networks for extreme multi-label text classification. IEEE Trans Knowl Data Eng 35(7):6698\u20136709","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5485_CR32","doi-asserted-by":"crossref","unstructured":"Xiao L, Zhang X, Jing L, et\u00a0al (2021) Does head label help for long-tailed multi-label text classification. In: Thirty-Fifth AAAI conference on artificial intelligence, pp 14103\u201314111","DOI":"10.1609\/aaai.v35i16.17660"},{"issue":"15","key":"5485_CR33","doi-asserted-by":"publisher","first-page":"11445","DOI":"10.1007\/s00521-023-08308-3","volume":"35","author":"S Qin","year":"2023","unstructured":"Qin S, Wu H, Zhou L et al (2023) Learning metric space with distillation for large-scale multi-label text classification. Neural Comput Appl 35(15):11445\u201311458","journal-title":"Neural Comput Appl"},{"issue":"8","key":"5485_CR34","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S et al (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"5485_CR35","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1080\/02626667.2021.2003367","volume":"67","author":"SA Hosseini","year":"2022","unstructured":"Hosseini SA, Shahri AA, Asheghi R (2022) Prediction of bedload transport rate using a block combined network structure. Hydrol Sci J 67(1):117\u2013128","journal-title":"Hydrol Sci J"},{"key":"5485_CR36","doi-asserted-by":"crossref","unstructured":"Xun G, Jha K, Sun J et\u00a0al (2020) Correlation networks for extreme multi-label text classification. In: Gupta R, Liu Y, Tang J, et\u00a0al (eds) KDD \u201920: The 26th ACM SIGKDD conference on knowledge discovery and data mining, Virtual Event, CA, USA, August 23-27, 2020. ACM, pp 1074\u20131082","DOI":"10.1145\/3394486.3403151"},{"key":"5485_CR37","unstructured":"Loza\u00a0Menc\u00eda E, F\u00fcrnkranz J (2008) An evaluation of efficient multilabel classification algorithms for large-scale problems in the legal domain. In: Proceedings of the LREC 2008 workshop on semantic processing of legal texts, Marrakech, Morocco, pp 23\u201332"},{"key":"5485_CR38","unstructured":"Zubiaga A (2009) Enhancing navigation on wikipedia with social tags. In: Wikimania 2009, Wikimedia Foundation"},{"key":"5485_CR39","doi-asserted-by":"publisher","unstructured":"Zhang D, Sensoy M, Makrehchi M et\u00a0al (2023) Uncertainty quantification for text classification. In: Proceedings of the 46th International ACM SIGIR conference on research and development in information retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023. ACM, pp 3426\u20133429. https:\/\/doi.org\/10.1145\/3539618.3594243","DOI":"10.1145\/3539618.3594243"},{"key":"5485_CR40","doi-asserted-by":"publisher","unstructured":"Chen W, Zhang B, Lu M (2020) Uncertainty quantification for multilabel text classification. WIREs Data Mining Knowl Discov. https:\/\/doi.org\/10.1002\/WIDM.1384","DOI":"10.1002\/WIDM.1384"},{"key":"5485_CR41","doi-asserted-by":"crossref","unstructured":"Peinelt N, Nguyen D, Liakata M (2020) tbert: Topic models and BERT joining forces for semantic similarity detection. In: Jurafsky D, Chai J, Schluter N et\u00a0al (eds) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020. Association for Computational Linguistics, pp 7047\u20137055","DOI":"10.18653\/v1\/2020.acl-main.630"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05485-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05485-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05485-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T12:14:17Z","timestamp":1718453657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05485-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":42,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5485"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05485-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"24 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}