{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T19:52:18Z","timestamp":1778961138993,"version":"3.51.4"},"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":[{"name":"Natural Science Foundation of Liaoning Province in China","award":["2020-MS-281"],"award-info":[{"award-number":["2020-MS-281"]}]},{"name":"Basic Research Project of Education Department of Liaoning Province in China","award":["JYTMS20230929"],"award-info":[{"award-number":["JYTMS20230929"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05498-8","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T08:01:51Z","timestamp":1715241711000},"page":"6285-6298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Collaborative learning of supervision and correlation for generalized zero-shot extreme multi-label learning"],"prefix":"10.1007","volume":"54","author":[{"given":"Fei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8081-6805","authenticated-orcid":false,"given":"Qing","family":"Ai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"issue":"4","key":"5498_CR1","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s10489-022-03655-5","volume":"53","author":"G Jung","year":"2023","unstructured":"Jung G, Shin J, Lee S (2023) Impact of preprocessing and word embedding on extreme multi-label patent classification tasks. Appl Intell 53(4):4047\u20134062","journal-title":"Appl Intell"},{"key":"5498_CR2","doi-asserted-by":"crossref","unstructured":"Tang P, Jiang M, Xia BN, Pitera JW, Welser J, Chawla NV (2020) Multi-label patent categorization with non-local attention-based graph convolutional network. Proceedings of the AAAI conference on artificial intelligence, pp 9024\u20139031","DOI":"10.1609\/aaai.v34i05.6435"},{"key":"5498_CR3","doi-asserted-by":"crossref","unstructured":"Prabhu Y, Kusupati A, Gupta N, Varma M (2020) Extreme Regression for Dynamic Search Advertising. Proceedings of the 13th international conference on web search and data mining, pp 456\u2013464","DOI":"10.1145\/3336191.3371768"},{"key":"5498_CR4","doi-asserted-by":"crossref","unstructured":"Chang W-C, Yu H-F, Zhong K, Yang Y, Dhillon IS (2020) Taming Pretrained Transformers for Extreme Multi-label Text Classification. Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3163\u20133171","DOI":"10.1145\/3394486.3403368"},{"key":"5498_CR5","doi-asserted-by":"crossref","unstructured":"Gupta N, Bohra S, Prabhu Y, Purohit S, Varma M (2021) Generalized Zero-Shot Extreme Multi-label Learning. Proceedings of the 27th ACM SIGKDD international conference on knowledge discovery & data mining, pp 527\u2013535","DOI":"10.1145\/3447548.3467426"},{"key":"5498_CR6","doi-asserted-by":"crossref","unstructured":"Xiong Y, Chang W-C, Hsieh C-J, Yu H-F, Dhillon I (2022) Extreme Zero-Shot Learning for Extreme Text Classification. Proceedings of the conference of the north american chapter of the association for computational linguistics: human language technologies, pp 5455\u20135468","DOI":"10.18653\/v1\/2022.naacl-main.399"},{"key":"5498_CR7","doi-asserted-by":"crossref","unstructured":"Zhang T, Xu Z, Medini T, Shrivastava A (2022) Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification. Find Assoc Comput Linguis EMNLP, pp 4937\u20134947","DOI":"10.18653\/v1\/2022.findings-emnlp.362"},{"key":"5498_CR8","unstructured":"Aggarwal P, Deshpande A, Narasimhan KR (2023) SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification. Int Conf Mach Learn pp 228\u2013247"},{"key":"5498_CR9","doi-asserted-by":"crossref","unstructured":"Simig D, Petroni F, Yanki P, Popat K, Du C, Riedel S, Yazdani M (2022) Open Vocabulary Extreme Classification Using Generative Models. Find Assoc Comput Linguis ACL, pp 1561\u20131583","DOI":"10.18653\/v1\/2022.findings-acl.123"},{"key":"5498_CR10","unstructured":"You R, Zhang Z, Wang Z, Dai S, Mamitsuka H, Zhu S (2019) AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. Adv Neural Inform Process Syst pp 5820\u20135830"},{"key":"5498_CR11","doi-asserted-by":"crossref","unstructured":"Jiang T, Wang D, Sun L, Yang H, Zhao Z, Zhuang F (2021) Lightxml: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification. Proceedings of the AAAI conference on artificial intelligence, pp 7987\u20137994","DOI":"10.1609\/aaai.v35i9.16974"},{"issue":"7","key":"5498_CR12","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":"5498_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ins.2022.11.158","volume":"622","author":"J Xiong","year":"2023","unstructured":"Xiong J, Yu L, Niu X, Leng Y (2023) Xrr: Extreme multi-label text classification with candidate retrieving and deep ranking. Inf Sci 622:115\u2013132","journal-title":"Inf Sci"},{"key":"5498_CR14","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Z, Qin Y, He D, Lin F (2023) Multi-aspect co-attentional collaborative filtering for extreme multi-label text classification. Knowl-Based Syst 260:110110","DOI":"10.1016\/j.knosys.2022.110110"},{"key":"5498_CR15","doi-asserted-by":"crossref","unstructured":"Yu H-F, Zhong K, Zhang J, Chang W-C, Dhillon IS (2022) Pecos: Prediction for enormous and correlated output spaces. J Mach Learn Res 23(98):1\u201332","DOI":"10.1145\/3534678.3542629"},{"key":"5498_CR16","doi-asserted-by":"crossref","unstructured":"Xu P, Xiao L, Liu B, Lu S, Jing L, Yu J (2023) Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification. Proceedings of the AAAI conference on artificial intelligence, pp 10602\u201310610","DOI":"10.1609\/aaai.v37i9.26259"},{"issue":"2","key":"5498_CR17","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10994-023-06468-w","volume":"113","author":"M Qaraei","year":"2024","unstructured":"Qaraei M, Babbar R (2024) Meta-classifier free negative sampling for extreme multilabel classification. Mach Learn 113(2):675\u2013697","journal-title":"Mach Learn"},{"issue":"11","key":"5498_CR18","doi-asserted-by":"publisher","first-page":"3953","DOI":"10.1007\/s10994-022-06228-2","volume":"111","author":"E Schultheis","year":"2022","unstructured":"Schultheis E, Babbar R (2022) Speeding-up one-versus-all training for extreme classification via mean-separating initialization. Mach Learn 111(11):3953\u20133976","journal-title":"Mach Learn"},{"issue":"5","key":"5498_CR19","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1007\/s11063-021-10444-7","volume":"54","author":"X Huang","year":"2022","unstructured":"Huang X, Chen B, Xiao L, Yu J, Jing L (2022) Label-aware document representation via hybrid attention for extreme multi-label text classification. Neural Process Lett 54(5):3601\u20133617","journal-title":"Neural Process Lett"},{"issue":"2","key":"5498_CR20","first-page":"1","volume":"13","author":"Q Li","year":"2022","unstructured":"Li Q, Peng H, Li J, Xia C, Yang R, Sun L, Yu PS, He L (2022) A survey on text classification: From traditional to deep learning. Acm Trans Intell Syst Technol 13(2):1\u201341","journal-title":"Acm Trans Intell Syst Technol"},{"key":"5498_CR21","doi-asserted-by":"crossref","unstructured":"Etter PA, Zhong K, Yu H-F, Ying L, Dhillon I (2022) Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees. Proceedings of the ACM Web Conference 2022:452\u2013461","DOI":"10.1145\/3485447.3511973"},{"key":"5498_CR22","doi-asserted-by":"crossref","unstructured":"Vu H-T, Nguyen M-T, Nguyen V-C, Pham M-H, Nguyen V-Q, Nguyen V-H (2023) Label-representative graph convolutional network for multi-label text classification. Appl Intell 53(12):14759\u201314774","DOI":"10.1007\/s10489-022-04106-x"},{"issue":"8","key":"5498_CR23","doi-asserted-by":"publisher","first-page":"13329","DOI":"10.1111\/exsy.13329","volume":"40","author":"S Basabain","year":"2023","unstructured":"Basabain S, Cambria E, Alomar K, Hussain A (2023) Enhancing arabic-text feature extraction utilizing label-semantic augmentation in few\/zero-shot learning. Expert Syst 40(8):13329","journal-title":"Expert Syst"},{"issue":"7","key":"5498_CR24","doi-asserted-by":"publisher","first-page":"8061","DOI":"10.1007\/s10489-022-03880-y","volume":"53","author":"W Liu","year":"2023","unstructured":"Liu W, Pang J, Li N, Yue F, Liu G (2023) Few-shot short-text classification with language representations and centroid similarity. Appl Intell 53(7):8061\u20138072","journal-title":"Appl Intell"},{"key":"5498_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109949","volume":"257","author":"W Fan","year":"2022","unstructured":"Fan W, Liang C, Wang T (2022) Contrastive semantic disentanglement in latent space for generalized zero-shot learning. Knowl-Based Syst 257:109949","journal-title":"Knowl-Based Syst"},{"key":"5498_CR26","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.1109\/TIP.2021.3120319","volume":"31","author":"C Zhang","year":"2022","unstructured":"Zhang C, Liang C, Zhao Y (2022) Exemplar-based, semantic guided zero-shot visual recognition. IEEE Trans Image Process 31:3056\u20133065","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"5498_CR27","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1109\/TPAMI.2022.3178914","volume":"45","author":"X Wang","year":"2022","unstructured":"Wang X, Jing L, Lyu Y, Guo M, Wang J, Liu H, Yu J, Zeng T (2022) Deep generative mixture model for robust imbalance classification. IEEE Trans Pattern Anal Mach Intell 45(3):2897\u20132912","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5498_CR28","doi-asserted-by":"crossref","unstructured":"Mishra A, Reddy SK, Mittal A, Murthy HA (2018) A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 2269\u201322698","DOI":"10.1109\/CVPRW.2018.00294"},{"key":"5498_CR29","doi-asserted-by":"crossref","unstructured":"Schonfeld E, Ebrahimi S, Sinha S, Darrell T, Akata Z (2019) Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8239\u20138247","DOI":"10.1109\/CVPR.2019.00844"},{"key":"5498_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y, Gao X, Han J, Shao L (2023) A discriminative cross-aligned variational autoencoder for zero-shot learning. IEEE Trans Cybern 53(6):3794\u20133805","DOI":"10.1109\/TCYB.2022.3164142"},{"key":"5498_CR31","unstructured":"Liu Y, Dang Y, Gao X, Han J, Shao L (2022) Zero-shot learning with attentive region embedding and enhanced semantics. IEEE Trans Neural Netw Learn Syst, pp 1\u201312"},{"key":"5498_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107352","volume":"107","author":"Y Luo","year":"2021","unstructured":"Luo Y, Wang X, Pourpanah F (2021) Dual vaegan: A generative model for generalized zero-shot learning. Appl Soft Comput 107:107352","journal-title":"Appl Soft Comput"},{"issue":"11","key":"5498_CR33","doi-asserted-by":"publisher","first-page":"6749","DOI":"10.1109\/TNNLS.2021.3083367","volume":"33","author":"C Tang","year":"2022","unstructured":"Tang C, He Z, Li Y, Lv J (2022) Zero-shot learning via structure-aligned generative adversarial network. IEEE Trans Neural Netw Learn Syst 33(11):6749\u20136762","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5498_CR34","doi-asserted-by":"publisher","first-page":"21841","DOI":"10.1007\/s10489-023-04623-3","volume":"53","author":"C Fan","year":"2023","unstructured":"Fan C, Chen W, Tian J, Li Y, He H, Jin Y (2023) Accurate use of label dependency in multi-label text classification through the lens of causality. Appl Intell 53:21841\u201321857","journal-title":"Appl Intell"},{"issue":"7","key":"5498_CR35","doi-asserted-by":"publisher","first-page":"8039","DOI":"10.1007\/s10489-022-03634-w","volume":"53","author":"Q Ai","year":"2023","unstructured":"Ai Q, Li F, Li X, Zhao J, Wang W, Gao Q, Zhao F (2023) An improved mltsvm using label-specific features with missing labels. Appl Intell 53(7):8039\u20138060","journal-title":"Appl Intell"},{"issue":"12","key":"5498_CR36","doi-asserted-by":"publisher","first-page":"9860","DOI":"10.1109\/TPAMI.2021.3136592","volume":"44","author":"J-Y Hang","year":"2021","unstructured":"Hang J-Y, Zhang M-L (2021) Collaborative learning of label semantics and deep label-specific features for multi-label classification. IEEE Trans Pattern Anal Mach Intell 44(12):9860\u20139871","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5498_CR37","doi-asserted-by":"crossref","unstructured":"Zhao W, Kong S, Bai J, Fink D, Gomes C (2021) HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders. Proceedings of the AAAI conference on artificial intelligence, pp 15016\u201315024","DOI":"10.1609\/aaai.v35i17.17762"},{"key":"5498_CR38","doi-asserted-by":"crossref","unstructured":"Loza Menc\u00eda E, F\u00fcrnkranz J (2008) Efficient pairwise multilabel classification for large-scale problems in the legal domain. Joint European conference on machine learning and knowledge discovery in databases, pp 50\u201365","DOI":"10.1007\/978-3-540-87481-2_4"},{"key":"5498_CR39","doi-asserted-by":"crossref","unstructured":"McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. Proceedings of the 7th ACM conference on Recommender systems, pp 165\u2013172","DOI":"10.1145\/2507157.2507163"},{"key":"5498_CR40","doi-asserted-by":"crossref","unstructured":"Prabhu Y, Varma M (2014) Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 263\u2013272","DOI":"10.1145\/2623330.2623651"},{"issue":"1\u20132","key":"5498_CR41","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1023\/A:1009982220290","volume":"1","author":"Y Yang","year":"1999","unstructured":"Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retr 1(1\u20132):69\u201390","journal-title":"Inf Retr"},{"key":"5498_CR42","unstructured":"Wang W, Wei F, Dong L, Bao H, Yang N, Zhou M (2020) MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. Adv Neural Inform Process Syst pp 5776\u20135788"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05498-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05498-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05498-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T11:57:58Z","timestamp":1731931078000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05498-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":42,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5498"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05498-8","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":"29 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 data used in this study were obtained through publicly available sources, and no ethical or informed consent considerations were required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}