{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:32:27Z","timestamp":1781047947641,"version":"3.54.1"},"reference-count":54,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,18]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Multi-label text classification (MLTC) aims to assign relevant labels to each document. Existing approaches primarily focus on semantic attributes manually extracted from a single data source, often neglecting the contextual subtleties of low-frequency tokens. Furthermore, each label carries its own unique semantic meaning, yet traditional models typically fail to capture the fine-grained relationships between labels and their intrinsic semantic information. The purpose of this study is to address these issues by proposing an enhanced dual-channel interaction network (EDCIN) to deeply explore the dependencies between text and labels.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>This approach is composed of three main components: smooth-guided data augmentation, dual-channel feature interaction and the classifier. Specifically, the smooth-guided data augmentation is designed to evaluate the relative importance of each word, enabling the adjustment of text feature distributions. The dual-channel feature interaction focuses on learning the complex relationships between the textual content and its associated labels.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>The EDCIN model demonstrates strong performance on both the AAPD and RCV1 data sets. On the AAPD data set, the values of P@n and nDCG@n are 86.84%, 62.71%, 42.04%, 83.05% and 86.07%, respectively. On the RCV1 data set, P@n and nDCG@n achieve 97.41%, 84.75%, 59.02%, 95.30% and 95.61%, respectively. These results highlight its effectiveness on MLTC tasks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>The smooth-guided data augmentation adjusts the distribution of text features and captures the complete semantic context. The dual-channel feature interaction thoroughly analyzes the intrinsic semantic information of labels by learning the complex feature relationships between the textual content and associated labels.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/ijwis-01-2025-0019","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T04:48:16Z","timestamp":1747716496000},"page":"453-470","source":"Crossref","is-referenced-by-count":1,"title":["EDCIN: enhanced dual-channel interaction network for multi-label text classification"],"prefix":"10.1108","volume":"21","author":[{"given":"Xiaoyun","family":"Liu","sequence":"first","affiliation":[{"name":"University of Jinan School of Information Science and Engineering, , Jinan, , and Shandong Key Laboratory of Ubiquitous Intelligent Computing, University of Jinan, Jinan, 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