{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T16:00:49Z","timestamp":1780848049801,"version":"3.54.1"},"reference-count":2,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a Conceptual Prototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, Conceptual Prototypical Contrastive Learning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. \u201c2-way 5-shot\u201d experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks.<\/jats:p>","DOI":"10.3233\/jifs-231570","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T10:50:34Z","timestamp":1697799034000},"page":"11963-11975","source":"Crossref","is-referenced-by-count":2,"title":["CPCL: Conceptual prototypical contrastive learning for Few-Shot text classification"],"prefix":"10.1177","volume":"45","author":[{"given":"Tao","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiquan","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiting","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-231570_ref7","first-page":"6407","article-title":"Hybrid attention-based prototypical networks for noisy few-shot relation classification","volume":"33","author":"Gao","year":"2019","journal-title":"in: AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-231570_ref14","first-page":"0","volume":"2","author":"Koch","year":"2015","journal-title":"ICML Deep Learning Workshop"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-231570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:16Z","timestamp":1777455736000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-231570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":2,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-231570","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}