{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:36:33Z","timestamp":1783528593381,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"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":["62077027"],"award-info":[{"award-number":["62077027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03896-4","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T02:03:08Z","timestamp":1659405788000},"page":"8761-8775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Label prompt for multi-label text classification"],"prefix":"10.1007","volume":"53","author":[{"given":"Rui","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zelong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingbing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haining","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"issue":"4","key":"3896_CR1","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1016\/j.eswa.2013.08.073","volume":"41","author":"W Li","year":"2014","unstructured":"Li W, Xu H (2014) Text-based emotion classification using emotion cause extraction. Expert Syst Appl 41(4):1742\u20131749","journal-title":"Expert Syst Appl"},{"key":"3896_CR2","doi-asserted-by":"crossref","unstructured":"Rios A, Kavuluru R (2015) Convolutional neural networks for biomedical text classification: application in indexing biomedical articles. In: Proceedings of the 6th ACM conference on bioinformatics, computational biology and health informatics, pp 258\u2013267","DOI":"10.1145\/2808719.2808746"},{"key":"3896_CR3","doi-asserted-by":"crossref","unstructured":"Cambria E, Olsher D, Rajagopal D (2014) Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. AAAI, p 1515\u20131521","DOI":"10.1609\/aaai.v28i1.8928"},{"key":"3896_CR4","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola JA, Hovy HE (2016) Hierarchical attention networks for document classification. HLT-NAACL, p 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"3896_CR5","doi-asserted-by":"crossref","unstructured":"Gopal S, Yang Y (2010) Multilabel classification with meta-level features. SIGIR, p 315\u2013322","DOI":"10.1145\/1835449.1835503"},{"key":"3896_CR6","unstructured":"Katakis I, Tsoumakas G, Vlahavas I (2008) Multilabel text classification for automated tag suggestion 18, 5. Citeseer"},{"key":"3896_CR7","doi-asserted-by":"crossref","unstructured":"Boutell RM, Luo J, Shen X, Brown MC (2004) Learning multi-label scene classification. Pattern Recognition, p 1757\u20131771","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"3896_CR8","doi-asserted-by":"crossref","unstructured":"Liu J, Chang W-C, Wu Y, Yang Y (2017) Deep learning for extreme multi-label text classification, p 115\u2013124","DOI":"10.1145\/3077136.3080834"},{"key":"3896_CR9","doi-asserted-by":"crossref","unstructured":"Xiao L, Zhang X, Jing L, Huang C, Song M (2021) Does head label help for long-tailed multi-label text classification 35(16), p 14103\u201314111","DOI":"10.1609\/aaai.v35i16.17660"},{"key":"3896_CR10","unstructured":"Yang P, Sun X, Li W, Ma S, Wu W, Wang H (2018) Sgm: Sequence generation model for multi-label classification, p 3915\u20133926"},{"key":"3896_CR11","doi-asserted-by":"crossref","unstructured":"Pappas N, Henderson J (2019) Gile: a generalized input-label embedding for text classification. TACL, p 139\u2013155","DOI":"10.1162\/tacl_a_00259"},{"key":"3896_CR12","doi-asserted-by":"crossref","unstructured":"Liu H, Yuan C, Wang X (2020) Label-wise document pre-training for multi-label text classification. international conference natural language processing, p 641\u2013653","DOI":"10.1007\/978-3-030-60450-9_51"},{"key":"3896_CR13","doi-asserted-by":"crossref","unstructured":"Zhu Y, Kwok TJ, Zhou ZH (2018) Multi-label learning with global and local label correlation. IEEE Transactions on Knowledge and Data Engineering, p 1081\u20131094","DOI":"10.1109\/TKDE.2017.2785795"},{"key":"3896_CR14","unstructured":"Ankit P, Muru S, Malaikannan S (2020) Multi-label text classification using attention-based graph neural network. ICAART. In: Proceedings of the 12th International conference on agents and artificial intelligence, vol 2, pp 494\u2013505"},{"key":"3896_CR15","unstructured":"Kenton Jdm-wc, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding, p 4171\u20134186"},{"key":"3896_CR16","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Advances in neural information processing systems 33:1877\u20131901","journal-title":"Advances in neural information processing systems"},{"key":"3896_CR17","unstructured":"Ding N, Chen Y, Han X, Xu G, Xie P, Zheng H-T, Liu Z, Li J, Kim H-G (2021) Prompt-learning for fine-grained entity typing. arXiv:2009.07118"},{"key":"3896_CR18","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H (2021) It\u2019s not just size that matters: Small language models are also few-shot learners, p 2339\u20132352","DOI":"10.18653\/v1\/2021.naacl-main.185"},{"key":"3896_CR19","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H (2021) Exploiting cloze-questions for few-shot text classification and natural language inference. EACL, p 255\u2013269","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"3896_CR20","doi-asserted-by":"crossref","unstructured":"H\u00fcllermeier E, F\u00fcrnkranz J, Cheng W, Brinker K (2008) Label ranking by learning pairwise preferences. Artif. Intell., p 1897\u20131916","DOI":"10.1016\/j.artint.2008.08.002"},{"key":"3896_CR21","doi-asserted-by":"crossref","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Machine Learning, p 333\u2013359","DOI":"10.1007\/s10994-011-5256-5"},{"key":"3896_CR22","doi-asserted-by":"crossref","unstructured":"Tsoumakas G, Vlahavas I (2007) Random k-labelsets: An ensemble method for multilabel classification, p 406\u2013417. Springer","DOI":"10.1007\/978-3-540-74958-5_38"},{"key":"3896_CR23","doi-asserted-by":"crossref","unstructured":"Chen G, Ye D, Xing Z, Chen J, Cambria E (2017) Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. IJCNN, p 2377\u20132383","DOI":"10.1109\/IJCNN.2017.7966144"},{"key":"3896_CR24","doi-asserted-by":"crossref","unstructured":"Barutcuoglu Z, Schapire ER, Troyanskaya GO (2006) Hierarchical multi-label prediction of gene function. Bioinformatics, p 830\u2013836","DOI":"10.1093\/bioinformatics\/btk048"},{"key":"3896_CR25","doi-asserted-by":"crossref","unstructured":"Zhang M-L, Zhang K (2010) Multi-label learning by exploiting label dependency. KDD, p 999\u20131008","DOI":"10.1145\/1835804.1835930"},{"key":"3896_CR26","doi-asserted-by":"crossref","unstructured":"Wang S, Wang J, Wang Z, Ji Q (2015) Multiple emotion tagging for multimedia data by exploiting high-order dependencies among emotions. IEEE Trans. Multimedia, p 2185\u20132197","DOI":"10.1109\/TMM.2015.2484966"},{"key":"3896_CR27","doi-asserted-by":"crossref","unstructured":"Wang S, Peng G, Zheng Z (2020) Capturing joint label distribution for multi-label classification through adversarial learning. IEEE Trans. Knowl. Data Eng., p 2310\u20132321","DOI":"10.1109\/TKDE.2019.2922603"},{"key":"3896_CR28","doi-asserted-by":"crossref","unstructured":"Scarselli F, Gori M, Tsoi CA, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Transactions on Neural Networks, p 61\u201380","DOI":"10.1109\/TNN.2008.2005605"},{"key":"3896_CR29","doi-asserted-by":"crossref","unstructured":"Xiao L, Huang X, Chen B, Jing L (2019) Label-specific document representation for multi-label text classification, p 466\u2013475","DOI":"10.18653\/v1\/D19-1044"},{"key":"3896_CR30","doi-asserted-by":"crossref","unstructured":"Wang Y, Yao Q, Kwok J, Ni ML (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Computing Surveys, p 1\u201334","DOI":"10.1145\/3386252"},{"key":"3896_CR31","unstructured":"Liu X, Zheng Y, Du Z, Ding M, Qian Y, Yang Z, Tang J (2021) Gpt understands, too. arXiv:2103.10385"},{"key":"3896_CR32","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhang Y (2021) Better few-shot text classification with pre-trained language model. ICANN, p 537\u2013548","DOI":"10.1007\/978-3-030-86340-1_43"},{"key":"3896_CR33","doi-asserted-by":"crossref","unstructured":"Shin T, Razeghi Y, Logan IV RL, Wallace E, Singh S (2020) Autoprompt: Eliciting knowledge from language models with automatically generated prompts. Empirical Methods in Natural Language Processing, p 4222\u20134235","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"3896_CR34","doi-asserted-by":"crossref","unstructured":"Schick T, Schmid H, Sch\u00fctze H (2020) Automatically identifying words that can serve as labels for few-shot text classification. COLING, p 5569\u20135578","DOI":"10.18653\/v1\/2020.coling-main.488"},{"key":"3896_CR35","doi-asserted-by":"crossref","unstructured":"Gao T, Fisch A, Chen D (2021) Making pre-trained language models better few-shot learners, p 3816\u20133830","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"3896_CR36","doi-asserted-by":"crossref","unstructured":"Li XL, Liang P (2021) Prefix-tuning: Optimizing continuous prompts for generation, p 4582\u20134597","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"3896_CR37","doi-asserted-by":"crossref","unstructured":"Lester B, Al-Rfou R, Constant N (2021) The power of scale for parameter-efficient prompt tuning. EMNLP, p 3045\u20133059","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"3896_CR38","doi-asserted-by":"crossref","unstructured":"Debole F, Sebastiani F (2005) An analysis of the relative hardness of reuters-21578 subsets: Research articles. Journal of the American Society for Information Science and Technology, p 584\u2013596","DOI":"10.1002\/asi.20147"},{"key":"3896_CR39","unstructured":"Dorottya D, Dana M-A, Jeongwoo K, Alan C, Gaurav N, Sujith R (2020) Goemotions: a dataset of fine-grained emotions. ACL, p 4040\u20134054"},{"key":"3896_CR40","doi-asserted-by":"crossref","unstructured":"Schapire ER, Singer Y (1998) Improved boosting algorithms using confidence-rated predictions. Machine Learning, p 80\u201391","DOI":"10.1145\/279943.279960"},{"key":"3896_CR41","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. EMNLP, p 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"issue":"8","key":"3896_CR42","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"3896_CR43","unstructured":"Loshchilov I, Hutter F (2018) Fixing weight decay regularization in adam. arXiv: Learning"},{"key":"3896_CR44","doi-asserted-by":"crossref","unstructured":"Jawahar G, Sagot B, Seddah D (2019) What does bert learn about the structure of language. ACL (1), p 3651\u20133657","DOI":"10.18653\/v1\/P19-1356"},{"key":"3896_CR45","unstructured":"Chen Z, Badrinarayanan V, Lee C-Y, Rabinovich A (2018) Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. international conference on machine learning, p 793\u2013802"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03896-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03896-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03896-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T09:14:11Z","timestamp":1682846051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03896-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":45,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3896"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03896-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,2]]},"assertion":[{"value":"13 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2022","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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}